GENERATING A SLEEP PROGRAM FOR A BED SYSTEM

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 63/700,465, filed on Sep. 27, 2024, and 63/733,364, filed on Dec. 12, 2024. The disclosure of the prior applications is hereby incorporated by reference in their entireties.

The present document relates to generating a temperature sleep program for a bed system.

BACKGROUND

In general, a bed is a piece of furniture used as a location to sleep or relax. Many modern beds include a soft mattress on a bed frame. The mattress may include springs, foam material, and/or an air chamber to support the weight of one or more occupants.

SUMMARY

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, can cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme (e.g., a temperature program) comprising multiple sequential temperature settings. The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from an initial temperature to a first temperature setting of the predetermined temperature scheme. The first temperature setting can be a low heating setting for a first period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system to a second temperature setting of the predetermined temperature scheme. The second temperature setting can be a low cooling setting for a second period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the second temperature setting to a third temperature setting of the predetermined temperature scheme. The third temperature setting can be a medium cooling setting for a third period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the third temperature setting to a fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be a low heating setting for a fourth period of time.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, can cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple sequential temperature settings. The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from an initial temperature to a first temperature setting of the predetermined temperature scheme. The first temperature setting can be an off setting for a first period of time. The instructions can further cause the at least one processor to refrain from altering the temperature of the portion of the bed system from the first temperature setting because a second temperature setting of the predetermined temperature scheme is also the off setting for a second period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the second temperature setting to a third temperature setting of the predetermined temperature scheme. The third temperature setting can be a medium cooling setting for a third period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the third temperature setting to a fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be a low cooling setting for a fourth period of time.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, can cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple sequential temperature settings. The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from an initial temperature to a first temperature setting of the predetermined temperature scheme. The first temperature setting can be a low heating setting for a first period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the first temperature setting to a second temperature setting of the predetermined temperature scheme. The second temperature setting can be a medium heating setting for a second period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the second temperature setting to a third temperature setting of the predetermined temperature scheme. The third temperature setting can be an off setting for a third period of time. The instructions can further cause the at least one processor to refrain from altering the temperature of the portion of the bed system from the third temperature setting because a fourth temperature setting of the predetermined temperature scheme is also the off setting for a fourth period of time.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple sequential temperature settings. The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from an initial temperature to a first temperature setting of the predetermined temperature scheme. The first temperature setting can be a medium heating setting for a first period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the first temperature setting to a second temperature setting of the predetermined temperature scheme. The second temperature setting can be a low heating setting for a second period of time. The instructions can further cause the at least one processor to refrain from altering the temperature of the portion of the bed system from the second temperature setting because a third temperature setting of the predetermined temperature scheme is also the low heating setting for a third period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system from the third temperature setting to a fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be the medium heating setting for a fourth period of time.

Embodiments described herein can include one or more optional features. For example, a percentage of restful sleep attained with the predetermined temperature scheme can be greater than 80% as compared to not using the predetermined temperature scheme. In some examples, the restful sleep is calculated using a ballistocardiography signal that provides a measure of movement level while the user is lying in bed, where for each 10-second window while the user is in bed, the level of movement is compared to a threshold, and if the level of activity in a given window is below the threshold, then the given window counts towards restful sleep. A duration of sleep attained by the user with the predetermined temperature scheme can be greater than 6.5 hours. A temperature of a microclimate of the user can be decreased during the third temperature setting for the third period of time. A distal skin temperature of the user can be increased during the first temperature setting and/or the second temperature setting. A core body temperature of the user can be increased during the fourth temperature setting. The bed system can include means to increase, decrease, and maintain a temperature of the user. The bed system can include means to accept voice commands to control one or more components. The bed system can include means to update firmware for temperature control. The first temperature setting and the second temperature setting can be configured to improve sleep quality by accelerating a time it takes for the user to fall asleep. The third temperature setting can be configured to improve sleep quality by lengthening a duration of the user staying asleep. The fourth temperature setting can be configured to improve sleep quality by accelerating the user to an alert wakeful state. The first temperature setting and the second temperature setting can be configured to improve sleep quality by accelerating a time it takes for the user to fall asleep, the third temperature setting can be configured to improve sleep quality by lengthening a duration of the user staying asleep, and the fourth temperature setting can be configured to improve sleep quality by accelerating the user to an alert wakeful state.

Some embodiments described herein include a method for improving sleep quality. The method can include configuring a temperature control system of a bed system. In some examples, configuring the temperature control system alters a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple temperature settings. In some examples, a first temperature setting alters the temperature of the portion of the bed system from an initial temperature to the first temperature setting of the predetermined temperature scheme. The first temperature setting can be a low heat setting for a first period of time. In some examples, a second temperature setting alters the temperature of the portion of the bed system from the first temperature setting to the second temperature setting of the predetermined temperature scheme. The second temperature setting can be a low cooling setting for a second period of time. In some examples, a third temperature setting alters the temperature of the portion of the bed system from the second temperature setting to the third temperature setting of the predetermined temperature scheme. The third temperature setting can be a medium cooling setting for a third period of time. In some examples, a fourth temperature setting alters the temperature of the portion of the bed system from the third temperature setting to the fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be a low heat setting for a fourth period of time.

Some embodiments described herein include a method for improving sleep quality. The method can include configuring a temperature control system of a bed system. In some examples, configuring the temperature control system alters a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising four temperature settings. In some examples, a first temperature setting alters the temperature of the portion of the bed system from an initial temperature to the first temperature setting of the predetermined temperature scheme. The first temperature setting can be an off setting for a first period of time. In some examples, a second temperature setting can refrain from altering the temperature of the portion of the bed system from the first temperature setting because the second temperature setting of the predetermined temperature scheme is also the off setting for a second period of time. In some examples, a third temperature setting alters the temperature of the portion of the bed system from the second temperature setting to the third temperature setting of the predetermined temperature scheme. The third temperature setting can be a medium cooling setting for a third period of time. In some examples, a fourth temperature setting alters the temperature of the portion of the bed system from the third temperature setting to the fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be a low cooling setting for a fourth period of time.

Some embodiments described herein include a method for improving sleep quality. The method can include configuring a temperature control system of a bed system. In some examples, configuring the temperature control system alters a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising four temperature settings. In some examples, a first temperature setting alters the temperature of the portion of the bed system from an initial temperature to the first temperature setting of the predetermined temperature scheme. The first temperature setting can be a low heating setting for a first period of time. In some examples, a second temperature setting alters the temperature of the portion of the bed system from the first temperature setting to the second temperature setting of the predetermined temperature scheme. The second temperature setting can be a medium heating setting for a second period of time. In some examples, a third temperature setting alters the temperature of the portion of the bed system from the second temperature setting to the third temperature setting of the predetermined temperature scheme. The third temperature setting can be an off setting for a third period of time. In some examples, a fourth temperature setting can refrain from altering the temperature of the portion of the bed system from the third temperature setting because the fourth temperature setting of the predetermined temperature scheme is also the off setting for a fourth period of time.

Some embodiments described herein include a method for improving sleep quality. The method can include configuring a temperature control system of a bed system. In some examples, configuring the temperature control system alters a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising four temperature settings. In some examples, a first temperature setting alters the temperature of the portion of the bed system from an initial temperature to the first temperature setting of the predetermined temperature scheme. The first temperature setting can be a medium heating setting for a first period of time. In some examples, a second temperature setting alters the temperature of the portion of the bed system from the first temperature setting to the second temperature setting of the predetermined temperature scheme. The second temperature setting can be a low heating setting for a second period of time. In some examples, a third temperature setting can refrain from altering the temperature of the portion of the bed system from the second temperature setting because the third temperature setting of the predetermined temperature scheme is also the low heating setting for a third period of time. In some examples, a fourth temperature setting alters the temperature of the portion of the bed system from the third temperature setting to the fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be the medium heating setting for a fourth period of time.

Embodiments described herein can include one or more optional features. For example, a percentage of restful sleep attained with the predetermined temperature scheme can be greater than 80% as compared to not using the predetermined temperature scheme. A duration of sleep attained by the user with the predetermined temperature scheme can be greater than 6.5 hours. A temperature of a microclimate of the user can be decreased during the third temperature setting for the third period of time. A distal skin temperature of the user can be increased during the first temperature setting and/or the second temperature setting. A core body temperature of the user can be increased during the fourth temperature setting. The method can include means to increase, decrease, and maintain a temperature of the user. The method can include means to accept voice commands to control one or more components. The method can include means to update firmware for temperature control. The first temperature setting and the second temperature setting can be configured to improve sleep quality by accelerating the user to falling asleep and staying asleep. The third temperature setting can be configured to improve sleep quality by lengthening a duration of the user staying asleep. The fourth temperature setting can be configured to improve sleep quality by accelerating the user to an alert wakeful state. The first temperature setting and the second temperature setting can be configured to improve sleep quality by accelerating the user to falling asleep and staying asleep, the third temperature setting can be configured to improve sleep quality by lengthening a duration of the user staying asleep, and the fourth temperature setting can be configured to improve sleep quality by accelerating the user to an alert wakeful state.

Some embodiments described herein include a bed system. The bed system includes a temperature control system. The temperature control system includes at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple temperature settings, wherein the predetermined temperature scheme is configured to improve sleep quality of a user of the bed system.

Embodiments described herein can include one or more optional features. For example, the predetermined temperature scheme can include a first temperature setting and a second temperature setting, and the first temperature setting and the second temperature setting can be configured to improve sleep quality by accelerating a time it takes for the user to fall asleep. The predetermined temperature scheme can include a third temperature setting, and the third temperature setting can be configured to improve sleep quality by lengthening a duration of the user staying asleep. The predetermined temperature scheme can include a fourth temperature setting, and the fourth temperature setting can be configured to improve sleep quality by accelerating the user to an alert wakeful state. The predetermined temperature scheme can include a first temperature setting, a second temperature setting, a third temperature setting, and a fourth temperature setting. The first temperature setting and the second temperature setting can be configured to improve sleep quality by accelerating the user to falling asleep and staying asleep, the third temperature setting can be configured to improve sleep quality by lengthening a duration of the user staying asleep, and the fourth temperature setting can be configured to improve sleep quality by accelerating the user to an alert wakeful state.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, can cause the at least one processor to receive a user input selecting a first temperature control scheme from a selectable set of a plurality of temperature control schemes and operate the temperature control system to alter a temperature of at least a portion of the bed system according to the first temperature control scheme. The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system.

Embodiments described herein can include one or more optional features. For example, each of the plurality of temperature control schemes could have been tested and proven in a clinical setting to improve sleep of multiple users as compared to other tested temperature control schemes. In some embodiments, the predetermined temperature control scheme tested was one of a) the first temperature setting was a low cooling setting, the second temperature setting was a medium cooling setting, and the third temperature setting was a low cooling setting, b) the first temperature setting was an off setting, the second temperature setting was the medium cooling setting, and the third temperature setting was the low cooling setting, or c) the first temperature setting was a low heating setting, the second temperature setting was the medium cooling setting, and the third temperature setting was the low heating setting.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, can cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple sequential temperature settings. The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system. The instructions can cause the at least one processor to alter the temperature of the portion of the bed system to a first temperature setting of the predetermined temperature scheme. The first temperature setting can be a low heating setting for a first period of time. The instructions can cause the at least one processor to alter the temperature of the portion of the bed system to a second temperature setting of the predetermined temperature scheme. The second temperature setting can be a low cooling setting for a second period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system to a third temperature setting of the predetermined temperature scheme. The third temperature setting can be a medium cooling setting for a third period of time. The instructions can further cause the at least one processor to alter the temperature of the portion of the bed system to a fourth temperature setting of the predetermined temperature scheme. The fourth temperature setting can be a low heating setting for a fourth period of time.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory storing instructions. The instructions, when executed by the at least one processor, can cause the at least one processor to operate the temperature control system to alter a temperature of at least a portion of the bed system according to a predetermined temperature scheme comprising multiple sequential temperature settings, The predetermined temperature scheme can be configured to improve sleep quality of a user of the bed system. The instructions can cause the at least one process to alter the temperature of the portion of the bed system from an initial temperature to a first temperature setting of the predetermined temperature scheme, after a first period of time and alter the temperature of the portion of the bed system from the first temperature setting to a second temperature setting of the predetermined temperature scheme after a second period of time.

Some embodiments described herein include a method for improving sleep quality. The method includes receiving historical data capturing one or more sleep sessions for a particular user. The historical data can include sleep data for the particular user for each of the one or more sleep sessions and temperature setting data for each of the one or more sleep sessions. The temperature setting data can include, for each sleep session of the one or more sleep sessions, an indication of a temperature setting applied to the particular user during one or more segments in the sleep session. The method further includes providing, the sleep data and the temperature setting data for each of the one or more sleep sessions as inputs to a model that generates an output indicative of a response of the particular users to temperature settings for the one or more segments of a predicted sleep session, generating a temperature program designed to promote improved sleep quality for the particular user based on the output, and configuring a temperature control system to alter a temperature of at least a portion of a bed system according to the temperature program.

Embodiments described herein can include one or more optional features. For example, the sleep data for each of the one or more sleep sessions can include: (a) a sleep duration; (b) a restful sleep duration; (c) a time to fall asleep; (d) heart rate data; (e) hear rate variability data; (f) breathing rate data; (g) a sleep score; or (h) any combination of (a)-(g). The model can be a linear model. The one or more segments can be defined using sleep onset time as a reference. The one or more segments can include a first segment corresponding for preconditioning the bed system, a second segment for falling asleep, a third segment for staying asleep, and a fourth segment for waking up. The one or more segments can include a first segment for falling asleep, a second segment for staying asleep, and a third segment for waking up. The temperature setting data can be encoded. The temperature settings can include high cooling, medium cooling, low cooling, off, low heating, medium heating, and high heating. The temperature setting data can be encoded linearly from a high cooling mode to a high heating mode. The historical data can include the sleep data and the temperature setting data for ten or more sleep sessions for the particular user. The model can be a non-linear model. The model can be a regression model. The model and the temperature program can be periodically updated as more sleep sessions for the particular user are captured. The output of the model can include a significance score that is calculated for each of the one or more segments of the predicted sleep session. In some examples, when the significance score for a particular segment in the one or more segments is less than a threshold, the temperature setting in the temperature program can be changed to explore for a temperature setting for the particular segment that improves the sleep quality for the particular user.

Some embodiments described herein include a bed system. The bed system can include a temperature control system. The temperature control system can include at least one processor and computer memory. The computer memory can store instructions that, when executed by the at least one processor, can cause the at least one processor to receive historical data capturing one or more sleep sessions for a particular user. The historical data can include sleep data for the particular user for each of the one or more sleep sessions and temperature setting data for each of the one or more sleep sessions. The temperature setting data can include, for each sleep session of the one or more sleep sessions, an indication of a temperature setting applied to the particular user during one or more segments in the sleep session. The instructions can further cause the at least one processor to provide, the sleep data and the temperature setting data for each of the one or more sleep sessions as inputs to a model that generates an output indicative of a response of the particular users to temperature settings for the one or more segments of a predicted sleep session, generate a temperature program to promote improved sleep quality for the particular user based on the output, and configure the temperature control system to alter a temperature of at least a portion of the bed system according to the temperature program.

Some embodiments described herein include a system. The system can include computing hardware configured to receive historical data capturing one or more sleep sessions for a particular user. The historical data can include sleep data for the particular user for each of the one or more sleep sessions and temperature setting data for each of the one or more sleep sessions. The temperature setting data can include, for each sleep session of the one or more sleep sessions, an indication of a temperature setting applied to the particular user during one or more segments in the sleep session. The computing hardware can be further configured to provide, the sleep data and the temperature setting data for each of the one or more sleep sessions as inputs to a model that generates an output indicative of a response of the particular users to temperature settings for the one or more segments of a predicted sleep session, generate a temperature program to promote improved sleep quality for the particular user based on the output, and transmit the temperature program to a temperature control system. The temperature control system can be configured to receiving the temperature program from the computer hardware and alter a temperature of at least a portion of a bed system according to the temperature program.

Embodiments described herein can include one or more optional features. For example, the computing hardware can include at least one processor and at least one memory. The computing hardware can include a server. The computing hardware can include a bed system controller.

Some embodiments described herein include a method for improving sleep quality. The method includes receiving targeted user data for a targeted user, comparing the targeted user data to user data for a plurality of users with known temperature programs to identify a most similar user to the targeted user, selecting a temperature program for the most similar user, and configuring a temperature control system to alter a temperature of at least a portion of a bed system for the targeted user according to the selected temperature program.

Embodiments described herein can include one or more optional features. For example, The targeted user data and the user data for the plurality of users with known temperature programs can include (a) demographic characteristics, (b) sleep metrics, (c) sleep behavior characteristics, or (c) any combination of (a), (b), or (c). In some examples, a similarity metric can be calculated between the targeted user and each of the plurality of users with the known temperature programs. In some examples, the similarity metric between the targeted user and the user with the known temperature programs can be calculated by comparing a first vector comprising features of the targeted user to a second vector comprising features of the user with the known temperature program. In some examples, the similarity metric is determined by calculating a dot product between the first vector and the second vector divided by the norm of the first vector and the second vector. The method can further include recording sleep data for one or more sleep sessions of the targeted user where the selected temperature program is implemented and generating a personalized temperature program for the targeted user based on the sleep data.

Some embodiments described herein include a system. The system can include computing hardware configured to receive targeted user data for a targeted user, compare the targeted user data to user data for a plurality of users with known temperature programs to identify a most similar user to the targeted user, and transmit a temperature program for the most similar user to a temperature control system. The temperature control system can be configured to receiving the temperature program of the computer hardware and alter a temperature of at least a portion of a bed system according to the temperature program.

Embodiments described herein can include one or more optional features. For example, the targeted user data and the user data for the plurality of users with known temperature programs can include: (a) demographic characteristics; (b) sleep metrics; (c) sleep behavior characteristics; or (c) any combination of (a), (b), or (c). In some examples, a similarity metric can be calculated between the targeted user and each of the plurality of users with the known temperature programs. In some examples, the similarity metric between the targeted user and the user with the known temperature programs can be calculated by comparing a first vector comprising features of the targeted user to a second vector comprising features of the user with the known temperature program. In some examples, the similarity metric can be determined by calculating a dot product between the first vector and the second vector divided by the norm of the first vector and the second vector. The system can be further configured to record sleep data for one or more sleep sessions of the targeted user where the selected temperature program is implemented and generate a personalized temperature program for the targeted user based on the sleep data. The computing hardware can include at least one processor and at least one memory. The computing hardware can include a server. The computing hardware can include a bed system controller.

The devices, system, and techniques described herein may provide one or more of the following advantages. For example, improvement of sleep quality by addressing the key components of falling asleep, staying asleep, and waking up refreshed. By facilitating an increase in distal skin temperature, the devices, system, and techniques described herein promote a natural decrease in core body temperature, which can shorten sleep onset latency. This feature is particularly beneficial for users who struggle with falling asleep quickly, as it enables a more efficient transition into sleep, contributing to overall better sleep quality.

Another advantage of the devices, system, and techniques described herein are capable to maintain sleep by managing core body temperature throughout the sleep cycle. By ensuring that core body temperature steadily decreases during the initial sleep cycle, the system supports prolonged sleep maintenance, which is essential for achieving restorative sleep. This consistent temperature regulation can help users stay asleep longer without interruptions, enhancing the quality of their rest and leading to better health and well-being.

Additionally, the devices, systems, and techniques described herein offer the advantage of enhancing the waking experience by, for example, promoting an increase in core body temperature as the user approaches their wake time. This controlled temperature rise is associated with improved alertness and a more refreshing wake-up experience. By aligning the user's wake time with a natural increase in core body temperature, the system can help users feel more alert and energized upon waking, improving their overall daily performance and satisfaction.

The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features, aspects and potential advantages will be apparent from the accompanying description and figures.

DESCRIPTION OF DRAWINGS

FIG. 1 shows an example air bed system.

FIG. 2 is a block diagram of an example of various components of an air bed system.

FIG. 3 shows an example environment including a bed in communication with devices located in and around a home.

FIGS. 4A and 4B are block diagrams of example data processing systems that can be associated with a bed.

FIGS. 5 and 6 are block diagrams of examples of motherboards that can be used in a data processing system associated with a bed.

FIG. 7 is a block diagram of an example of a daughterboard that can be used in a data processing system associated with a bed.

FIG. 8 is a block diagram of an example of a motherboard with no daughterboard that can be used in a data processing system associated with a bed.

FIG. 9A is a block diagram of an example of a sensory array that can be used in a data processing system associated with a bed.

FIG. 9B is a schematic top view of a bed having an example of a sensor strip with one or more sensors that can be used in a data processing system associated with the bed.

FIG. 9C is a schematic diagram of an example bed with force sensors located at the bottom of legs of the bed.

FIG. 10 is a block diagram of an example of a control array that can be used in a data processing system associated with a bed

FIG. 11 is a block diagram of an example of a computing device that can be used in a data processing system associated with a bed.

FIGS. 12-16 are block diagrams of example cloud services that can be used in a data processing system associated with a bed.

FIG. 17 is a block diagram of an example of using a data processing system that can be associated with a bed to automate peripherals around the bed.

FIG. 18 is a schematic diagram that shows an example of a computing device and a mobile computing device.

FIG. 19 is a schematic of the layout and positioning of temperature sensors within a smart bed.

FIG. 20 is a schematic for generating a sleep program for a bed system.

FIGS. 21A, 21B, and 21C depict the design of a temperature program to enhance sleep quality.

FIG. 22 depicts an overview of the data analysis methods used in the study. In-home data analysis methods:

FIG. 23 depicts a schematic for sleep session inclusion in the AT analysis.

FIGS. 24A and 24B. Sleep onset latency versus first temperature setting. (A) considers all temperature settings and intensities thereof. (B) Aggregated into heating and cooling regardless of intensity.

FIGS. 25A-25D depict a change in sleep depth during the falling asleep process (starting from lights-off) depending on the first temperature setting. (A) Mean normalized sleep depth curves for each temperature setting (dashed lines represent the standard errors). (B) P-value comparing cooling versus heating. (C) P-value comparing Off versus heating. (D) P-value comparing cooling versus Off.

FIGS. 26A-26D depict a change in sleep depth during the falling asleep process (starting from sleep onset) depending on the first temperature setting. (A) Mean normalized sleep depth curves for each temperature setting (dashed lines represent the standard errors). (B) P-value comparing cooling versus heating. (C) P-value comparing Off versus heating. (D) P-value comparing cooling versus Off.

FIGS. 27A-27D depict a change in heartrate during the falling asleep process depending on the first temperature setting taking as reference “lights off”. (A) Mean normalized heartrate curves for each temperature setting (dashed lines indicate the standard errors). (B) P-value comparing cooling versus heating. Significant values are indicated in orange. (C) P-value comparing Off versus heating. Significant values are identified by blue bars. (D) P-value comparing Off versus Cooling. Significant values are indicated by blue bars. Although certain significant values may be illustrated and described throughout this document, other values can also be used in various embodiments.

FIGS. 28A-28D. Change in heartrate during falling asleep depending on the first temperature setting taking as reference “sleep onset time”. (A) Mean normalized heartrate curves for each temperature setting (dashed lines indicate the standard errors). (B) P-value comparing cooling versus heating. Significant values are indicated in orange. (C) P-value comparing Off versus heating. Significant values are indicated in orange. (D) P-value comparing Off versus cooling. Significant values would be identified by blue bars.

FIGS. 29A-29D depict a change in foot temperature during the falling asleep process depending on the first temperature setting. Lights off was taken as reference. (B) Statistical comparison heating versus cooling (significant values are shown as orange bars). (C) Statistical comparison heating versus Off (significant values are shown as orange bars). (D) Statistical comparison cooling versus off (significant values are shown as blue bars).

FIGS. 30A-30D depict a change in foot temperature during falling asleep depending on the first temperature setting. Sleep onset was taken as reference. (B) P-value comparing cooling versus heating. Significant values are indicated in orange. (C) P-value comparing Off versus heating. Significant values are indicated in orange. (D) P-value comparing Off versus cooling. Significant values would be identified by blue bars.

FIGS. 31A-31C depict the duration of wake after sleep onset (WASO) for each temperature setting.

FIG. 32 depicts a distribution of sleep stages vs temperature setting for the first three hours of in-lab sessions with the bar graphs representing cooling on the left, off in the middle, and heating on the right.

FIG. 33 depicts a distribution of percent sleep stages vs temperature setting for the first three hours of in-lab sessions.

FIG. 34 depicts a distribution of sleep stages vs temperature setting for the second three-hour segment (which coincides with the delivery of the second in-lab temperature setting) across all in-lab sessions with the bar graphs representing cooling on the left, off in the middle, and heating on the right.

FIG. 35 depicts a distribution of sleep stages vs temperature setting for the third three-hour segment (which coincides with the delivery of the third in-lab temperature setting) across all in-lab sessions with the bar graphs representing cooling on the left, off in the middle, and heating on the right.

FIG. 36 depicts the results of the foregoing analysis and identifies the four programs that were associated with higher objective sleep quality.

FIGS. 37A-37F illustrate example user interfaces for configuring a temperature program.

FIG. 38 illustrates an example user interface flow for configuring a warming and deep sleep cooling routine.

FIG. 39 illustrates an example user interface flow for configuring an all-night cooling routine.

FIG. 40 illustrates an example user interface flow for configuring a personalized cooling routine.

FIG. 41 illustrates an example user interface flow for customizing a temperature routine.

FIG. 42 illustrates an example process that uses ballistocardiography (BCG) signals from a pressure sensor in a bed system to determine sleep metrics for a user.

FIG. 43 illustrates an example flow diagram illustrating a method for generating a model for optimizing a temperature program at an individual level.

FIG. 44 illustrates a table presenting an example output for the model illustrated and described in FIG. 43.

FIG. 45 illustrates an example flow diagram illustrating a method for identifying a temperature program for a targeted user.

FIG. 46 illustrates that activation of the smart temperature programs increased the duration of restful sleep relative to the OFF condition during the winter months.

FIG. 47 shows the effect of the smart temperature programs (STP) on restful sleep duration in users who self-reported insomnia.

FIG. 48 depicts restful sleep duration for users who self-report sleep apnea under four smart temperature programs (STP) compared to the OFF condition.

FIG. 49 shows restful sleep duration for users who self-reported chronic pain under four smart temperature programs (STP) (X-axis) compared to the OFF condition.

FIG. 50 depicts restful sleep duration for users reporting chronic stress under four smart temperature programs (STP) compared to the OFF condition.

FIG. 51 shows restful sleep duration for users self-reporting depression under four smart temperature programs (STP) compared to the OFF condition.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The systems, devices, and techniques described herein provide an approach for optimizing sleep quality by addressing several components of sleep: falling asleep, maintaining sleep, and waking up refreshed. Disclosed herein are systems, devices, and techniques that describe the significance of distal and core body temperatures to achieve these sleep components. For example, by facilitating an increase in distal skin temperature, the systems, devices, and techniques described herein promote a decrease in core body temperature, which can be associated with reduced sleep onset latency. Additionally, maintaining a steady decrease in core body temperature during the initial sleep cycle supports prolonged sleep maintenance. As users approach their wake time, an increase in core body temperature is encouraged to enhance alertness and improve the waking experience. These systems, devices, and techniques disclosed herein allow for a tailored sleep environment that adapts to the physiological needs of the user, contributing to overall improved sleep quality and user satisfaction.

Example Airbed Hardware

FIG. 1 shows an example air bed system 100 that includes a bed 112. The bed 112 can be a mattress that includes at least one air chamber 114 surrounded by a resilient border 116 and encapsulated by bed ticking 118. The resilient border 116 can comprise any suitable material, such as foam. In some embodiments, the resilient border 116 can combine with a top layer or layers of foam (not shown in FIG. 1) to form an upside down foam tub. In other embodiments, mattress structure can be varied as suitable for the application.

As illustrated in FIG. 1, the bed 112 can be a two chamber design having first and second fluid chambers, such as a first air chamber 114A and a second air chamber 114B. Sometimes, the bed 112 can include chambers for use with fluids other than air that are suitable for the application. For example, the fluids can include liquid. In some embodiments, such as single beds or kids' beds, the bed 112 can include a single air chamber 114A or 114B or multiple air chambers 114A and 114B. Although not depicted, sometimes, the bed 112 can include additional air chambers.

The first and second air chambers 114A and 114B can be in fluid communication with a pump 120. The pump 120 can be in electrical communication with a remote control 122 via control box 124. The control box 124 can include a wired or wireless communications interface for communicating with one or more devices, including the remote control 122. The control box 124 can be configured to operate the pump 120 to cause increases and decreases in the fluid pressure of the first and second air chambers 114A and 114B based upon commands input by a user using the remote control 122. In some implementations, the control box 124 is integrated into a housing of the pump 120. Moreover, sometimes, the pump 120 can be in wireless communication (e.g., via a home network, WiFi, Bluetooth, or other wireless network) with a mobile device via the control box 124. The mobile device can include but is not limited to the user's smartphone, cell phone, laptop, tablet, computer, wearable device, home automation device, or other computing device. A mobile application can be presented at the mobile device and provide functionality for the user to control the bed 112 and view information about the bed 112. The user can input commands in the mobile application presented at the mobile device. The inputted commands can be transmitted to the control box 124, which can operate the pump 120 based upon the commands.

The remote control 122 can include a display 126, an output selecting mechanism 128, a pressure increase button 129, and a pressure decrease button 130. The remote control 122 can include one or more additional output selecting mechanisms and/or buttons. The display 126 can present information to the user about settings of the bed 112. For example, the display 126 can present pressure settings of both the first and second air chambers 114A and 114B or one of the first and second air chambers 114A and 114B. Sometimes, the display 126 can be a touch screen, and can receive input from the user indicating one or more commands to control pressure in the first and second air chambers 114A and 114B and/or other settings of the bed 112.

The output selecting mechanism 128 can allow the user to switch air flow generated by the pump 120 between the first and second air chambers 114A and 114B, thus enabling control of multiple air chambers with a single remote control 122 and a single pump 120. For example, the output selecting mechanism 128 can by a physical control (e.g., switch or button) or an input control presented on the display 126. Alternatively, separate remote control units can be provided for each air chamber 114A and 114B and can each include the ability to control multiple air chambers. Pressure increase and decrease buttons 129 and 130 can allow the user to increase or decrease the pressure, respectively, in the air chamber selected with the output selecting mechanism 128. Adjusting the pressure within the selected air chamber can cause a corresponding adjustment to the firmness of the respective air chamber. In some embodiments, the remote control 122 can be omitted or modified as appropriate for an application.

FIG. 2 is a block diagram of an example of various components of an air bed system. These components can be used in the example air bed system 100. The control box 124 can include a power supply 134, a processor 136, a memory 137, a switching mechanism 138, and an analog to digital (A/D) converter 140. The switching mechanism 138 can be, for example, a relay or a solid state switch. In some implementations, the switching mechanism 138 can be located in the pump 120 rather than the control box 124. The pump 120 and the remote control 122 can be in two-way communication with the control box 124. The pump 120 includes a motor 142, a pump manifold 143, a relief valve 144, a first control valve 145A, a second control valve 145B, and a pressure transducer 146. The pump 120 is fluidly connected with the first air chamber 114A and the second air chamber 114B via a first tube 148A and a second tube 148B, respectively. The first and second control valves 145A and 145B can be controlled by switching mechanism 138, and are operable to regulate the flow of fluid between the pump 120 and first and second air chambers 114A and 114B, respectively.

In some implementations, the pump 120 and the control box 124 can be provided and packaged as a single unit. In some implementations, the pump 120 and the control box 124 can be provided as physically separate units. The control box 124, the pump 120, or both can be integrated within or otherwise contained within a bed frame, foundation, or bed support structure that supports the bed 112. Sometimes, the control box 124, the pump 120, or both can be located outside of a bed frame, foundation, or bed support structure (as shown in the example in FIG. 1).

The air bed system 100 in FIG. 2 includes the two air chambers 114A and 114B and the single pump 120 of the bed 112 depicted in FIG. 1. However, other implementations can include an air bed system having two or more air chambers and one or more pumps incorporated into the air bed system to control the air chambers. For example, a separate pump can be associated with each air chamber. As another example, a pump can be associated with multiple chambers. A first pump can be associated with air chambers that extend longitudinally from a left side to a midpoint of the air bed system 100 and a second pump can be associated with air chambers that extend longitudinally from a right side to the midpoint of the air bed system 100. Separate pumps can allow each air chamber to be inflated or deflated independently and/or simultaneously.

Additional pressure transducers can also be incorporated into the air bed system 100 such that a separate pressure transducer can be associated with each air chamber.

As an illustrative example, in use, the processor 136 can send a decrease pressure command to one of air chambers 114A or 114B, and the switching mechanism 138 can convert the low voltage command signals sent by the processor 136 to higher operating voltages sufficient to operate the relief valve 144 of the pump 120 and open the respective control valve 145A or 145B. Opening the relief valve 144 can allow air to escape from the air chamber 114A or 114B through the respective air tube 148A or 148B. During deflation, the pressure transducer 146 can send pressure readings to the processor 136 via the A/D converter 140. The A/D converter 140 can receive analog information from pressure transducer 146 and can convert the analog information to digital information useable by the processor 136. The processor 136 can send the digital signal to the remote control 122 to update the display 126 to convey the pressure information to the user. The processor 136 can also send the digital signal to other devices in wired or wireless communication with the air bed system, including but not limited to mobile devices described herein. The user can then view pressure information associated with the air bed system at their device instead of at, or in addition to, the remote control 122.

As another example, the processor 136 can send an increase pressure command. The pump motor 142 can be energized in response to the increase pressure command and send air to the designated one of the air chambers 114A or 114B through the air tube 148A or 148B via electronically operating the corresponding valve 145A or 145B. While air is being delivered to the designated air chamber 114A or 114B to increase the chamber firmness, the pressure transducer 146 can sense pressure within the pump manifold 143. The pressure transducer 146 can send pressure readings to the processor 136 via the A/D converter 140. The processor 136 can use the information received from the A/D converter 140 to determine the difference between the actual pressure in air chamber 114A or 114B and the desired pressure. The processor 136 can send the digital signal to the remote control 122 to update display 126.

Generally speaking, during an inflation or deflation process, the pressure sensed within the pump manifold 143 can provide an approximation of the actual pressure within the respective air chamber that is in fluid communication with the pump manifold 143. An example method includes turning off the pump 120, allowing the pressure within the air chamber 114A or 114B and the pump manifold 143 to equalize, then sensing the pressure within the pump manifold 143 with the pressure transducer 146. Providing a sufficient amount of time to allow the pressures within the pump manifold 143 and chamber 114A or 114B to equalize can result in pressure readings that are accurate approximations of actual pressure within air chamber 114A or 114B. In some implementations, the pressure of the air chambers 114A and/or 114B can be continuously monitored using multiple pressure sensors (not shown). The pressure sensors can be positioned within the air chambers. The pressure sensors can also be fluidly connected to the air chambers, such as along the air tubes 148A and 148B.

In some implementations, information collected by the pressure transducer 146 can be analyzed to determine various states of a user laying on the bed 112. For example, the processor 136 can use information collected by the pressure transducer 146 to determine a heartrate or a respiration rate for the user. As an illustrative example, the user can be laying on a side of the bed 112 that includes the chamber 114A. The pressure transducer 146 can monitor fluctuations in pressure of the chamber 114A, and this information can be used to determine the user's heartrate and/or respiration rate. As another example, additional processing can be performed using the collected data to determine a sleep state of the user (e.g., awake, light sleep, deep sleep). For example, the processor 136 can determine when the user falls asleep and, while asleep, the various sleep states (e.g., sleep stages) of the user. Based on the determined heartrate, respiration rate, and/or sleep states of the user, the processor 136 can determine information about the user's sleep quality. The processor 136 can, for example, determine how well the user slept during a particular sleep cycle. The processor 136 can also determine user sleep cycle trends. Accordingly, the processor 136 can generate recommendations to improve the user's sleep quality and overall sleep cycle. Information that is determined about the user's sleep cycle (e.g., heartrate, respiration rate, sleep states, sleep quality, recommendations to improve sleep quality, etc.) can be transmitted to the user's mobile device and presented in a mobile application, as described above.

Additional information associated with the user of the air bed system 100 that can be determined using information collected by the pressure transducer 146 includes user motion, presence on a surface of the bed 112, weight, heart arrhythmia, snoring, partner snore, and apnea. One or more other health conditions of the user can also be determined based on the information collected by the pressure transducer 146. Taking user presence detection for example, the pressure transducer 146 can be used to detect the user's presence on the bed 112, e.g., via a gross pressure change determination and/or via one or more of a respiration rate signal, heartrate signal, and/or other biometric signals. Detection of the user's presence can be beneficial to determine, by the processor 136, adjustment(s) to make to settings of the bed 112 (e.g., adjusting a firmness when the user is present to a user-preferred firmness setting) and/or peripheral devices (e.g., turning off lights when the user is present, activating a heating or cooling system, etc.).

For example, a simple pressure detection process can identify an increase in pressure as an indication that the user is present. As another example, the processor 136 can determine that the user is present if the detected pressure increases above a specified threshold (so as to indicate that a person or other object above a certain weight is positioned on the bed 112). As yet another example, the processor 136 can identify an increase in pressure in combination with detected slight, rhythmic fluctuations in pressure as corresponding to the user being present. The presence of rhythmic fluctuations can be identified as being caused by respiration or heart rhythm (or both) of the user. The detection of respiration or a heartbeat can distinguish between the user being present on the bed and another object (e.g., a suitcase, a pet, a pillow, etc.) being placed thereon.

In some implementations, pressure fluctuations can be measured at the pump 120. For example, one or more pressure sensors can be located within one or more internal cavities of the pump 120 to detect pressure fluctuations within the pump 120. The fluctuations detected at the pump 120 can indicate pressure fluctuations in the chambers 114A and/or 114B. One or more sensors located at the pump 120 can be in fluid communication with the chambers 114A and/or 114B, and the sensors can be operative to determine pressure within the chambers 114A and/or 114B. The control box 124 can be configured to determine at least one vital sign (e.g., heartrate, respiratory rate) based on the pressure within the chamber 114A or the chamber 114B.

The control box 124 can also analyze a pressure signal detected by one or more pressure sensors to determine a heartrate, respiration rate, and/or other vital signs of the user lying or sitting on the chamber 114A and/or 114B. More specifically, when a user lies on the bed 112 and is positioned over the chamber 114A, each of the user's heart beats, breaths, and other movements (e.g., hand, arm, leg, foot, or other gross body movements) can create a force on the bed 112 that is transmitted to the chamber 114A. As a result of this force input, a wave can propagate through the chamber 114A and into the pump 120. A pressure sensor located at the pump 120 can detect the wave, and thus the pressure signal outputted by the sensor can indicate a heartrate, respiratory rate, or other information regarding the user.

With regard to sleep state, the air bed system 100 can determine the user's sleep state by using various biometric signals such as heartrate, respiration, and/or movement of the user. While the user is sleeping, the processor 136 can receive one or more of the user's biometric signals (e.g., heartrate, respiration, motion, etc.) and can determine the user's present sleep state based on the received biometric signals. In some implementations, signals indicating fluctuations in pressure in one or both of the chambers 114A and 114B can be amplified and/or filtered to allow for more precise detection of heartrate and respiratory rate.

Sometimes, the processor 136 can receive additional biometric signals of the user from one or more other sensors or sensor arrays positioned on or otherwise integrated into the air bed system 100. For example, one or more sensors can be attached or removably attached to a top surface of the air bed system 100 and configured to detect signals such as heartrate, respiration rate, and/or motion. The processor 136 can combine biometric signals received from pressure sensors located at the pump 120, the pressure transducer 146, and/or the sensors positioned throughout the air bed system 100 to generate accurate and more precise information about the user and their sleep quality.

Sometimes, the control box 124 can perform a pattern recognition algorithm or other calculation based on the amplified and filtered pressure signal(s) to determine the user's heartrate and/or respiratory rate. For example, the algorithm or calculation can be based on assumptions that a heartrate portion of the signal has a frequency in a range of 0.5-4.0 Hz and that a respiration rate portion of the signal has a frequency in a range of less than 1 Hz. Sometimes, the control box 124 can use one or more machine learning models to determine the user's health information. The models can be trained using training data that includes training pressure signals and expected heartrates and/or respiratory rates. Sometimes, the control box 124 can determine user health information by using a lookup table that corresponds to sensed pressure signals.

The control box 124 can also be configured to determine other characteristics of the user based on the received pressure signal, such as blood pressure, tossing and turning movements, rolling movements, limb movements, weight, presence or lack of presence of the user, and/or the identity of the user.

For example, the pressure transducer 146 can be used to monitor the air pressure in the chambers 114A and 114B of the bed 112. If the user on the bed 112 is not moving, the air pressure changes in the air chamber 114A or 114B can be relatively minimal, and can be attributable to respiration and/or heartbeat. When the user on the bed 112 is moving, however, the air pressure in the mattress can fluctuate by a much larger amount. The pressure signals generated by the pressure transducer 146 and received by the processor 136 can be filtered and indicated as corresponding to motion, heartbeat, or respiration. The processor 136 can attribute such fluctuations in air pressure to the user's sleep quality. Such attributions can be determined based on applying one or more machine learning models and/or algorithms to the pressure signals. For example, if the user shifts and turns a lot during a sleep cycle (for example, in comparison to historic trends of the user's sleep cycles), the processor 136 can determine that the user experienced poor sleep during that particular sleep cycle.

In some implementations, rather than performing the data analysis in the control box 124 with the processor 136, a digital signal processor (DSP) can be provided to analyze the data collected by the pressure transducer 146. Alternatively, the collected data can be sent to a cloud-based computing system for remote analysis.

In some implementations, the example air bed system 100 further includes a temperature controller configured to increase, decrease, or maintain a temperature of the bed 112, for example for the comfort of the user. For example, a pad (e.g., mat, layer, etc.) can be placed on top of or be part of the bed 112, or can be placed on top of or be part of one or both of the chambers 114A and 114B. Air can be pushed through the pad and vented to cool off the user on the bed 112. Additionally or alternatively, the pad can include a heating element used to keep the user warm. In some implementations, the temperature controller can receive temperature readings from the pad. The temperature controller can determine whether the temperature readings are less than or greater than some threshold range and/or value. Based on this determination, the temperature controller can actuate components to push air through the pad to cool off the user or activate the heating element. In some implementations, separate pads are used for different sides of the bed 112 (e.g., corresponding to the locations of the chambers 114A and 114B) to provide for differing temperature control for the different sides of the bed 112. Each pad can be selectively controlled by the temperature controller to provide cooling or heating preferred by each user on the different sides of the bed 112. For example, a first user on a left side of the bed 112 can prefer to have their side of the bed 112 cooled during the night while a second user on a right side of the bed 112 can prefer to have their side of the bed 112 warmed during the night.

In some implementations, the user of the air bed system 100 can use an input device, such as the remote control 122 or a mobile device as described above, to input a desired temperature for a surface of the bed 112 (or for a portion of the surface of the bed 112, for example at a foot region, a lumbar or waist region, a shoulder region, and/or a head region of the bed 112). The desired temperature can be encapsulated in a command data structure that includes the desired temperature and also identifies the temperature controller as the desired component to be controlled. The command data structure can then be transmitted via Bluetooth or another suitable communication protocol (e.g., WiFi, a local network, etc.) to the processor 136. In various examples, the command data structure is encrypted before being transmitted. The temperature controller can then configure its elements to increase or decrease the temperature of the pad depending on the temperature input provided at the remote control 122 by the user.

In some implementations, data can be transmitted from a component back to the processor 136 or to one or more display devices, such as the display 126 of the remote controller 122. For example, the current temperature as determined by a sensor element of a temperature controller, the pressure of the bed, the current position of the foundation or other information can be transmitted to control box 124. The control box 124 can transmit this information to the remote control 122 to be displayed to the user (e.g., on the display 126). As described above, the control box 124 can also transmit the received information to a mobile device to be displayed in a mobile application or other graphical user interface (GUI) to the user.

In some implementations, the example air bed system 100 further includes an adjustable foundation and an articulation controller configured to adjust the position of the bed 112 by adjusting the adjustable foundation supporting the bed. For example, the articulation controller can adjust the bed 112 from a flat position to a position in which a head portion of a mattress of the bed is inclined upward (e.g., to facilitate a user sitting up in bed and/or watching television). The bed 112 can also include multiple separately articulable sections. As an illustrative example, the bed 112 can include one or more of a head portion, a lumbar/waist portion, a leg portion, and/or a foot portion, all of which can be separately articulable. As another example, portions of the bed 112 corresponding to the locations of the chambers 114A and 114B can be articulated independently from each other, to allow one user positioned on the bed 112 surface to rest in a first position (e.g., a flat position or other desired position) while a second user rests in a second position (e.g., a reclining position with the head raised at an angle from the waist or another desired position). Separate positions can also be set for two different beds (e.g., two twin beds placed next to each other). The foundation of the bed 112 can include more than one zone that can be independently adjusted.

Sometimes, the bed 112 can be adjusted to one or more user-defined positions based on user input and/or user preferences. For example, the bed 112 can automatically adjust, by the articulation controller, to one or more user-defined settings. As another example, the user can control the articulation controller to adjust the bed 112 to one or more user-defined positions. Sometimes, the bed 112 can be adjusted to one or more positions that may provide the user with improved or otherwise improve sleep and sleep quality. For example, a head portion on one side of the bed 112 can be automatically articulated, by the articulation controller, when one or more sensors of the air bed system 100 detect that a user sleeping on that side of the bed 112 is snoring. As a result, the user's snoring can be mitigated so that the snoring does not wake up another user sleeping in the bed 112.

In some implementations, the bed 112 can be adjusted using one or more devices in communication with the articulation controller or instead of the articulation controller. For example, the user can change positions of one or more portions of the bed 112 using the remote control 122 described above. The user can also adjust the bed 112 using a mobile application or other graphical user interface presented at a mobile computing device of the user.

The articulation controller can also provide different levels of massage to one or more portions of the bed 112 for one or more users. The user(s) can adjust one or more massage settings for the portions of the bed 112 using the remote control 122 and/or a mobile device in communication with the air bed system 100.

Example of a Bed in a Bedroom Environment

FIG. 3 shows an example environment 300 including a bed 302 in communication with devices located in and around a home. In the example shown, the bed 302 includes pump 304 for controlling air pressure within two air chambers 306a and 306b (as described above). The pump 304 additionally includes circuitry 334 for controlling inflation and deflation functionality performed by the pump 304. The circuitry 334 is programmed to detect fluctuations in air pressure of the air chambers 306a-b and use the detected fluctuations to identify bed presence of a user 308, the user's sleep state, movement, and biometric signals (e.g., heartrate, respiration rate). The detected fluctuations can also be used to detect when the user 308 is snoring and whether the user 308 has sleep apnea or other health conditions. The detected fluctuations can also be used to determine an overall sleep quality of the user 308.

In the example shown, the pump 304 is located within a support structure of the bed 302 and the control circuitry 334 for controlling the pump 304 is integrated with the pump 304. In some implementations, the control circuitry 334 is physically separate from the pump 304 and is in wireless or wired communication with the pump 304. In some implementations, the pump 304 and/or control circuitry 334 are located outside of the bed 302. In some implementations, various control functions can be performed by systems located in different physical locations. For example, circuitry for controlling actions of the pump 304 can be located within a pump casing of the pump 304 while control circuitry 334 for performing other functions associated with the bed 302 can be located in another portion of the bed 302, or external to the bed 302. The control circuitry 334 located within the pump 304 can also communicate with control circuitry 334 at a remote location through a LAN or WAN (e.g., the internet). The control circuitry 334 can also be included in the control box 124 of FIGS. 1 and 2.

In some implementations, one or more devices other than, or in addition to, the pump 304 and control circuitry 334 can be utilized to identify user bed presence, sleep state, movement, biometric signals, and other information (e.g., sleep quality, health related) about the user 308. For example, the bed 302 can include a second pump, with each pump connected to a respective one of the air chambers 306a-b. For example, the pump 304 can be in fluid communication with the air chamber 306b to control inflation and deflation of the air chamber 306b as well as detect user signals for a user located over the air chamber 306b. The second pump can be in fluid communication with the air chamber 306a and used to control inflation and deflation of the air chamber 306a as well as detect user signals for a user located over the air chamber 306a.

As another example, the bed 302 can include one or more pressure sensitive pads or surface portions operable to detect movement, including user presence, motion, respiration, and heartrate. A first pressure sensitive pad can be incorporated into a surface of the bed 302 over a left portion of the bed 302, where a first user would normally be located during sleep, and a second pressure sensitive pad can be incorporated into the surface of the bed 302 over a right portion of the bed 302, where a second user would normally be located. The movement detected by the pressure sensitive pad(s) or surface portion(s) can be used by control circuitry 334 to identify user sleep state, bed presence, or biometric signals for each user. The pressure sensitive pads can also be removable rather than incorporated into the surface of the bed 302.

The bed 302 can also include one or more temperature sensors and/or array of sensors operable to detect temperatures in microclimates of the bed 302. Detected temperatures in different microclimates of the bed 302 can be used by the control circuitry 334 to determine one or more modifications to the user 308's sleep environment. For example, a temperature sensor located near a core region of the bed 302 where the user 308 rests can detect high temperature values. Such high temperature values can indicate that the user 308 is warm. To lower the user's body temperature in this microclimate, the control circuitry 334 can determine that a cooling element of the bed 302 can be activated. As another example, the control circuitry 334 can determine that a cooling unit in the home can be automatically activated to cool an ambient temperature in the environment 300.

The control circuitry 334 can also process a combination of signals sensed by different sensors that are integrated into, positioned on, or otherwise in communication with the bed 112. For example, pressure and temperature signals can be processed by the control circuitry 334 to more accurately determine one or more health conditions of the user 308 and/or sleep quality of the user 308. Acoustic signals detected by one or more microphones or other audio sensors can also be used in combination with pressure or motion sensors in order to determine when the user 308 snores, whether the user 308 has sleep apnea, and/or overall sleep quality of the user 308. Combinations of one or more other sensed signals are also possible for the control circuitry 334 to more accurately determine one or more health and/or sleep conditions of the user 308.

Accordingly, information detected by one or more sensors or other components of the bed 112 (e.g., motion information) can be processed by the control circuitry 334 and provided to one or more user devices, such as a user device 310 for presentation to the user 308 or to other users. The information can be presented in a mobile application or other graphical user interface at the user device 310. The user 308 can view different information that is processed and/or determined by the control circuitry 334 and based the signals that are detected by components of the bed 302. For example, the user 308 can view their overall sleep quality for a particular sleep cycle (e.g., the previous night), historic trends of their sleep quality, and health information. The user 308 can also adjust one or more settings of the bed 302 (e.g., increase or decrease pressure in one or more regions of the bed 302, incline or decline different regions of the bed 302, turn on or off massage features of the bed 302, etc.) using the mobile application that is presented at the user device 310.

In the example depicted in FIG. 3, the user device 310 is a mobile phone; however, the user device 310 can also be any one of a tablet, personal computer, laptop, a smartphone, a smart television (e.g., a television 312), a home automation device, or other user device capable of wired or wireless communication with the control circuitry 334, one or more other components of the bed 302, and/or one or more devices in the environment 300. The user device 310 can be in communication with the control circuitry 334 of the bed 302 through a network or through direct point-to-point communication. For example, the control circuitry 334 can be connected to a LAN (e.g., through a WiFi router) and communicate with the user device 310 through the LAN. As another example, the control circuitry 334 and the user device 310 can both connect to the Internet and communicate through the Internet. For example, the control circuitry 334 can connect to the Internet through a WiFi router and the user device 310 can connect to the Internet through communication with a cellular communication system. As another example, the control circuitry 334 can communicate directly with the user device 310 through a wireless communication protocol, such as Bluetooth. As yet another example, the control circuitry 334 can communicate with the user device 310 through a wireless communication protocol, such as ZigBee, Z-Wave, infrared, or another wireless communication protocol suitable for the application. As another example, the control circuitry 334 can communicate with the user device 310 through a wired connection such as, for example, a USB connector, serial/RS232, or another wired connection suitable for the application.

As mentioned above, the user device 310 can display a variety of information and statistics related to sleep, or user 308's interaction with the bed 302. For example, a user interface displayed by the user device 310 can present information including amount of sleep for the user 308 over a period of time (e.g., a single evening, a week, a month, etc.), amount of deep sleep, ratio of deep sleep to restless sleep, time lapse between the user 308 getting into bed and falling asleep, total amount of time spent in the bed 302 for a given period of time, heartrate over a period of time, respiration rate over a period of time, or other information related to user interaction with the bed 302 by the user 308 or one or more other users. In some implementations, information for multiple users can be presented on the user device 310, for example information for a first user positioned over the air chamber 306a can be presented along with information for a second user positioned over the air chamber 306b. In some implementations, the information presented on the user device 310 can vary according to the age of the user 308 so that the information presented evolves with the age of the user 308.

The user device 310 can also be used as an interface for the control circuitry 334 of the bed 302 to allow the user 308 to enter information and/or adjust one or more settings of the bed 302. The information entered by the user 308 can be used by the control circuitry 334 to provide better information to the user 308 or to various control signals for controlling functions of the bed 302 or other devices. For example, the user 308 can enter information such as weight, height, and age of the user 308. The control circuitry 334 can use this information to provide the user 308 with a comparison of the user 308's tracked sleep information to sleep information of other people having similar weights, heights, and/or ages as the user 308. The control circuitry 308 can also use this information to accurately determine overall sleep quality and/or health of the user 308 based on information detected by components (e.g., sensors) of the bed 302.

The user 308 may also use the user device 310 as an interface for controlling air pressure of the air chambers 306a and 306b, various recline or incline positions of the bed 302, temperature of one or more surface temperature control devices of the bed 302, or for allowing the control circuitry 334 to generate control signals for other devices (as described below).

The control circuitry 334 may also communicate with other devices or systems, including but not limited to the television 312, a lighting system 314, a thermostat 316, a security system 318, home automation devices, and/or other household devices (e.g., an oven 322, a coffee maker 324, a lamp 326, a nightlight 328). Other examples of devices and/or systems include a system for controlling window blinds 330, devices for detecting or controlling states of one or more doors 332 (such as detecting if a door is open, detecting if a door is locked, or automatically locking a door), and a system for controlling a garage door 320 (e.g., control circuitry 334 integrated with a garage door opener for identifying an open or closed state of the garage door 320 and for causing the garage door opener to open or close the garage door 320). Communications between the control circuitry 334 and other devices can occur through a network (e.g., a LAN or the Internet) or as point-to-point communication (e.g., Bluetooth, radio communication, or a wired connection). Control circuitry 334 of different beds 302 can also communicate with different sets of devices. For example, a kid's bed may not communicate with and/or control the same devices as an adult bed. In some embodiments, the bed 302 can evolve with the age of the user such that the control circuitry 334 of the bed 302 communicates with different devices as a function of age of the user of that bed 302.

The control circuitry 334 can receive information and inputs from other devices/systems and use the received information and inputs to control actions of the bed 302 and/or other devices. For example, the control circuitry 334 can receive information from the thermostat 316 indicating a current environmental temperature for a house or room in which the bed 302 is located. The control circuitry 334 can use the received information (along with other information, such as signals detected from one or more sensors of the bed 302) to determine if a temperature of all or a portion of the surface of the bed 302 should be raised or lowered. The control circuitry 334 can then cause a heating or cooling mechanism of the bed 302 to raise or lower the temperature of the surface of the bed 302. The control circuitry 334 can also cause a heating or cooling unit of the house or room in which the bed 302 is located to raise or lower the ambient temperature surrounding the bed 302. Thus, by adjusting the temperature of the bed 302 and/or the room in which the bed 302 is located, the user 308 can experience more improved sleep quality and comfort.

As an example, the user 308 can indicate a desired sleeping temperature of 74 degrees while a second user of the bed 302 indicates a desired sleeping temperature of 72 degrees. The thermostat 316 can transmit signals indicating room temperature at predetermined times to the control circuitry 334. The thermostat 316 can also send a continuous stream of detected temperature values of the room to the control circuitry 334. The transmitted signal(s) can indicate to the control circuitry 334 that the current temperature of the bedroom is 72 degrees. The control circuitry 334 can identify that the user 308 has indicated a desired sleeping temperature of 74 degrees, and can accordingly send control signals to a heating pad located on the user 308's side of the bed to raise the temperature of the portion of the surface of the bed 302 where the user 308 is located until the user 308's desired temperature is achieved. Moreover, the control circuitry 334 can sent control signals to the thermostat 316 and/or a heating unit in the house to raise the temperature in the room in which the bed 302 is located.

The control circuitry 334 can generate control signals to control other devices and propagate the control signals to the other devices. The control signals can be generated based on information collected by the control circuitry 334, including information related to user interaction with the bed 302 by the user 308 and/or one or more other users. Information collected from other devices other than the bed 302 can also be used when generating the control signals. For example, information relating to environmental occurrences (e.g., environmental temperature, environmental noise level, and environmental light level), time of day, time of year, day of the week, or other information can be used when generating control signals for various devices in communication with the control circuitry 334 of the bed 302.

For example, information on the time of day can be combined with information relating to movement and bed presence of the user 308 to generate control signals for the lighting system 314. The control circuitry 334 can, based on detected pressure signals of the user 308 on the bed 302, determine when the user 308 is presently in the bed 302 and when the user 308 falls asleep. Once the control circuitry 334 determines that the user has fallen asleep, the control circuitry 334 can transmit control signals to the lighting system 314 to turn off lights in the room in which the bed 302 is located, to lower the window blinds 330 in the room, and/or to activate the nightlight 328. Moreover, the control circuitry 334 can receive input from the user 308 (e.g., via the user device 310) that indicates a time at which the user 308 would like to wake up. When that time approaches, the control circuitry 334 can transmit control signals to one or more devices in the environment 300 to control devices that may cause the user 308 to wake up. For example, the control signals can be sent to a home automation device that controls multiple devices in the home. The home automation device can be instructed, by the control circuitry 334, to raise the window blinds 330, turn off the nightlight 328, turn on lighting beneath the bed 302, start the coffee machine 324, change a temperature in the house via the thermostat 316, or perform some other home automation. The home automation device can also be instructed to activate an alarm that can cause the user 308 to wake up. Sometimes, the user 308 can input information at the user device 310 that indicates what actions can be taken by the home automation device or other devices in the environment 300.

In some implementations, rather than or in addition to providing control signals for other devices, the control circuitry 334 can provide collected information (e.g., information related to user movement, bed presence, sleep state, or biometric signals) to one or more other devices to allow the one or more other devices to utilize the collected information when generating control signals. For example, the control circuitry 334 of the bed 302 can provide information relating to user interactions with the bed 302 by the user 308 to a central controller (not shown) that can use the provided information to generate control signals for various devices, including the bed 302.

The central controller can, for example, be a hub device that provides a variety of information about the user 308 and control information associated with the bed 302 and other devices in the house. The central controller can include sensors that detect signals that can be used by the control circuitry 334 and/or the central controller to determine information about the user 308 (e.g., biometric or other health data, sleep quality). The sensors can detect signals including such as ambient light, temperature, humidity, volatile organic compound(s), pulse, motion, and audio. These signals can be combined with signals detected by sensors of the bed 302 to determine accurate information about the user 308's health and sleep quality. The central controller can provide controls (e.g., user-defined, presets, automated, user initiated) for the bed 302, determining and viewing sleep quality and health information, a smart alarm clock, a speaker or other home automation device, a smart picture frame, a nightlight, and one or more mobile applications that the user 308 can install and use at the central controller. The central controller can include a display screen that outputs information and receives user input. The display can output information such as the user 308's health, sleep quality, weather, security integration features, lighting integration features, heating and cooling integration features, and other controls to automate devices in the house. The central controller can operate to provide the user 308 with functionality and control of multiple different types of devices in the house as well as the user 308's bed 302.

As an illustrative example of FIG. 3, the control circuitry 334 integrated with the pump 304 can detect a feature of a mattress of the bed 302, such as an increase in pressure in the air chamber 306b, and use this detected increase to determine that the user 308 is present on the bed 302. The control circuitry 334 may also identify a heartrate or respiratory rate for the user 308 to identify that the increased pressure is due to a person sitting, laying, or resting on the bed 302, rather than an inanimate object (e.g., a suitcase) having been placed on the bed 302. In some implementations, the information indicating user bed presence can be combined with other information to identify a current or future likely state for the user 308. For example, a detected user bed presence at 11:00 am can indicate that the user is sitting on the bed (e.g., to tie her shoes, or to read a book) and does not intend to go to sleep, while a detected user bed presence at 10:00 pm can indicate that the user 308 is in bed for the evening and is intending to fall asleep soon. As another example, if the control circuitry 334 detects that the user 308 has left the bed 302 at 6:30 am (e.g., indicating that the user 308 has woken up for the day), and then later detects presence of the user 308 at 7:30 am on the bed 302, the control circuitry 334 can use this information that the newly detected presence is likely temporary (e.g., while the user 308 ties her shoes before heading to work) rather than an indication that the user 308 is intending to stay on the bed 302 for an extended period of time.

If the control circuitry 334 determines that the user 308 is likely to remain on the bed 302 for an extended period of time, the control circuitry 334 can determine one or more home automation controls that can aid the user 308 in falling asleep and experience improved sleep quality throughout the user 308's sleep cycle. For example, the control circuitry 334 can communicate with security system 318 to ensure that doors are locked. The control circuitry 334 can communicate with the oven 322 to ensure that the oven 322 is turned off. The control circuitry 334 can also communicate with the lighting system 314 to dim or otherwise turn off lights in the room in which the bed 302 is located and/or throughout the house, and the control circuitry 334 can communicate with the thermostat 316 to ensure that the house is at a desired temperature of the user 308. The control circuitry 334 can also determine one or more adjustments that can be made to the bed 302 to facilitate the user 308 falling asleep and staying asleep (e.g., changing a position of one or more regions of the bed 302, foot warming, massage features, pressure/firmness in one or more regions of the bed 302, etc.).

In some implementations, the control circuitry 334 may use collected information (including information related to user interaction with the bed 302 by the user 308, environmental information, time information, and user input) to identify use patterns for the user 308. For example, the control circuitry 334 can use information indicating bed presence and sleep states for the user 308 collected over a period of time to identify a sleep pattern for the user. The control circuitry 334 can identify that the user 308 generally goes to bed between 9:30 pm and 10:00 pm, generally falls asleep between 10:00 pm and 11:00 pm, and generally wakes up between 6:30 am and 6:45 am, based on information indicating user presence and biometrics for the user 308 collected over a week or a different time period. The control circuitry 334 can use identified patterns of the user 308 to better process and identify user interactions with the bed 302.

Given the above example user bed presence, sleep, and wake patterns for the user 308, if the user 308 is detected as being on the bed 302 at 3:00 pm, the control circuitry 334 can determine that the user 308's presence on the bed 302 is temporary, and use this determination to generate different control signals than if the control circuitry 334 determined the user 308 was in bed for the evening (e.g., at 3:00 pm, a head region of the bed 302 can be raised to facilitate reading or watching TV while in the bed 302, whereas in the evening, the bed 302 can be adjusted to a flat position to facilitate falling asleep). As another example, if the control circuitry 334 detects that the user 308 got out of bed at 3:00 am, the control circuitry 334 can use identified patterns for the user 308 to determine the user has gotten up temporarily (e.g., to use the bathroom, get a glass of water). The control circuitry 334 can turn on underbed lighting to assist the user 308 in carefully moving around the bed 302 and room. By contrast, if the control circuitry 334 identifies that the user 308 got out of the bed 302 at 6:40 am, the control circuitry 334 can determine the user 308 is up for the day and generate a different set of control signals (e.g., the control circuitry 334 can turn on light 326 near the bed 302 and/or raise the window blinds 330). For other users, getting out of the bed 302 at 3:00 am can be a normal wake-up time, which the control circuitry 334 can learn and respond to accordingly. Moreover, if the bed 302 is occupied by two users, the control circuitry 334 can learn and respond to the patterns of each of the users.

The bed 302 can also generate control signals based on communication with one or more devices. As an illustrative example, the control circuitry 334 can receive an indication from the television 312 that the television 312 is turned on. If the television 312 is located in a different room than the bed 302, the control circuitry 334 can generate a control signal to turn the television 312 off upon making a determination that the user 308 has gone to bed for the evening or otherwise is remaining in the room with the bed 302. If presence of the user 308 is detected on the bed 302 during a particular time range (e.g., between 8:00 pm and 7:00 am) and persists for longer than a threshold period of time (e.g., 10 minutes), the control circuitry 334 can determine the user 308 is in bed for the evening. If the television 312 is on, as described above, the control circuitry 334 can generate a control signal to turn the television 312 off. The control signals can be transmitted to the television (e.g., through a directed communication link or through a network, such as WiFi). As another example, rather than turning off the television 312 in response to detection of user bed presence, the control circuitry 334 can generate a control signal that causes the volume of the television 312 to be lowered by a pre-specified amount.

As another example, upon detecting that the user 308 has left the bed 302 during a specified time range (e.g., between 6:00 am and 8:00 am), the control circuitry 334 can generate control signals to cause the television 312 to turn on and tune to a pre-specified channel (e.g., the user 308 indicated a preference for watching morning news upon getting out of bed). The control circuitry 334 can accordingly generate and transmit the control signal to the television 312 (which can be stored at the control circuitry 334, the television 312, or another location). As another example, upon detecting that the user 308 has gotten up for the day, the control circuitry 334 can generate and transmit control signals to cause the television 312 to turn on and begin playing a previously recorded program from a digital video recorder (DVR) in communication with the television 312.

As another example, if the television 312 is in the same room as the bed 302, the control circuitry 334 may not cause the television 312 to turn off in response to detection of user bed presence. Rather, the control circuitry 334 can generate and transmit control signals to cause the television 312 to turn off in response to determining that the user 308 is asleep. For example, the control circuitry 334 can monitor biometric signals of the user 308 (e.g., motion, heartrate, respiration rate) to determine that the user 308 has fallen asleep. Upon detecting that the user 308 is sleeping, the control circuitry 334 generates and transmits a control signal to turn the television 312 off. As another example, the control circuitry 334 can generate the control signal to turn off the television 312 after a threshold period of time has passed since the user 308 has fallen asleep (e.g., 10 minutes after the user has fallen asleep). As another example, the control circuitry 334 generates control signals to lower the volume of the television 312 after determining that the user 308 is asleep. As yet another example, the control circuitry 334 generates and transmits a control signal to cause the television to gradually lower in volume over a period of time and then turn off in response to determining that the user 308 is asleep. Any of the control signals described above in reference to the television 312 can also be determined by the central controller previously described.

In some implementations, the control circuitry 334 can similarly interact with other media devices, such as computers, tablets, mobile phones, smart phones, wearable devices, stereo systems, etc. For example, upon detecting that the user 308 is asleep, the control circuitry 334 can generate and transmit a control signal to the user device 310 to cause the user device 310 to turn off, or turn down the volume on a video or audio file being played by the user device 310.

The control circuitry 334 can additionally communicate with the lighting system 314, receive information from the lighting system 314, and generate control signals for controlling functions of the lighting system 314. For example, upon detecting user bed presence on the bed 302 during a certain time frame (e.g., between 8:00 pm and 7:00 am) that lasts for longer than a threshold period of time (e.g., 10 minutes), the control circuitry 334 of the bed 302 can determine that the user 308 is in bed for the evening and generate control signals to cause lights in one or more rooms other than the room in which the bed 302 is located to switch off. The control circuitry 334 can generate and transmit control signals to turn off lights in all common rooms, but not in other bedrooms. As another example, the control signals can indicate that lights in all rooms other than the room in which the bed 302 is located are to be turned off, while one or more lights located outside of the house containing the bed 302 are to be turned on. The control circuitry 334 can generate and transmit control signals to cause the nightlight 328 to turn on in response to determining user 308 bed presence or that the user 308 is asleep. The control circuitry 334 can also generate first control signals for turning off a first set of lights (e.g., lights in common rooms) in response to detecting user bed presence, and second control signals for turning off a second set of lights (e.g., lights in the room where the bed 302 is located) when detecting that the user 308 is asleep.

In some implementations, in response to determining that the user 308 is in bed for the evening, the control circuitry 334 of the bed 302 can generate control signals to cause the lighting system 314 to implement a sunset lighting scheme in the room in which the bed 302 is located. A sunset lighting scheme can include, for example, dimming the lights (either gradually over time, or all at once) in combination with changing the color of the light in the bedroom environment, such as adding an amber hue to the lighting in the bedroom. The sunset lighting scheme can help to put the user 308 to sleep when the control circuitry 334 has determined that the user 308 is in bed for the evening. Sometimes, the control signals can cause the lighting system 314 to dim the lights or change color of the lighting in the bedroom environment, but not both.

The control circuitry 334 can also implement a sunrise lighting scheme when the user 308 wakes up in the morning. The control circuitry 334 can determine that the user 308 is awake for the day, for example, by detecting that the user 308 has gotten off the bed 302 (e.g., is no longer present on the bed 302) during a specified time frame (e.g., between 6:00 am and 8:00 am). The control circuitry 334 can also monitor movement, heartrate, respiratory rate, or other biometric signals of the user 308 to determine that the user 308 is awake or is waking up, even though the user 308 has not gotten out of bed. If the control circuitry 334 detects that the user is awake or waking up during a specified timeframe, the control circuitry 334 can determine that the user 308 is awake for the day. The specified timeframe can be, for example, based on previously recorded user bed presence information collected over a period of time (e.g., two weeks) that indicates that the user 308 usually wakes up for the day between 6:30 am and 7:30 am. In response to the control circuitry 334 determining that the user 308 is awake, the control circuitry 334 can generate control signals to cause the lighting system 314 to implement the sunrise lighting scheme in the bedroom in which the bed 302 is located. The sunrise lighting scheme can include, for example, turning on lights (e.g., the lamp 326, or other lights in the bedroom). The sunrise lighting scheme can further include gradually increasing the level of light in the room where the bed 302 is located (or in one or more other rooms). The sunrise lighting scheme can also include only turning on lights of specified colors. The sunrise lighting scheme can include lighting the bedroom with blue light to gently assist the user 308 in waking up and becoming active.

The control circuitry 334 may also generate different control signals for controlling actions of components depending on a time of day that user interactions with the bed 302 are detected. For example, the control circuitry 334 can use historical user interaction information to determine that the user 308 usually falls asleep between 10:00 pm and 11:00 pm and usually wakes up between 6:30 am and 7:30 am on weekdays. The control circuitry 334 can use this information to generate a first set of control signals for controlling the lighting system 314 if the user 308 is detected as getting out of bed at 3:00 am (e.g., turn on lights that guide the user 308 to a bathroom or kitchen) and to generate a second set of control signals for controlling the lighting system 314 if the user 308 is detected as getting out of bed after 6:30 am.

In some implementations, if the user 308 is detected as getting out of bed prior to a specified morning rise time for the user 308, the control circuitry 334 can cause the lighting system 314 to turn on lights that are dimmer than lights that are turned on by the lighting system 314 if the user 308 is detected as getting out of bed after the specified morning rise time. Causing the lighting system 314 to only turn on dim lights when the user 308 gets out of bed during the night (e.g., prior to normal rise time for the user 308) can prevent other occupants of the house from being woken up by the lights while still allowing the user 308 to see in order to reach their destination in the house.

The historical user interaction information for interactions between the user 308 and the bed 302 can be used to identify user sleep and awake timeframes. For example, user bed presence times and sleep times can be determined for a set period of time (e.g., two weeks, a month, etc.). The control circuitry 334 can identify a typical time range or timeframe in which the user 308 goes to bed, a typical timeframe for when the user 308 falls asleep, and a typical timeframe for when the user 308 wakes up (and in some cases, different timeframes for when the user 308 wakes up and when the user 308 actually gets out of bed). Buffer time may be added to these timeframes. For example, if the user is identified as typically going to bed between 10:00 pm and 10:30 pm, a buffer of a half hour in each direction can be added to the timeframe such that any detection of the user getting in bed between 9:30 pm and 11:00 pm is interpreted as the user 308 going to bed for the evening. As another example, detection of bed presence of the user 308 starting from a half hour before the earliest typical time that the user 308 goes to bed extending until the typical wake up time (e.g., 6:30 am) for the user 308 can be interpreted as the user 308 going to bed for the evening. For example, if the user 308 typically goes to bed between 10:00 pm and 10:30 pm, if the user 308's bed presence is sensed at 12:30 am one night, that can be interpreted as the user 308 getting into bed for the evening even though this is outside of the user 308's typical timeframe for going to bed because it has occurred prior to the user 308's normal wake up time. In some implementations, different timeframes are identified for different times of year (e.g., earlier bed time during winter vs. summer) or at different times of the week (e.g., user 308 wakes up earlier on weekdays than on weekends).

The control circuitry 334 can distinguish between the user 308 going to bed for an extended period (e.g., for the night) as opposed to being present on the bed 302 for a shorter period (e.g., for a nap) by sensing duration of presence of the user 308 (e.g., by detecting pressure and/or temperature signals of the user 308 on the bed 302 by sensors integrated into the bed 302). In some examples, the control circuitry 334 can distinguish between the user 308 going to bed for an extended period (e.g., for the night) versus going to bed for a shorter period (e.g., for a nap) by sensing duration of the user 308's sleep. The control circuitry 334 can set a time threshold whereby if the user 308 is sensed on the bed 302 for longer than the threshold, the user 308 is considered to have gone to bed for the night. In some examples, the threshold can be about 2 hours, whereby if the user 308 is sensed on the bed 302 for greater than 2 hours, the control circuitry 334 registers that as an extended sleep event. In other examples, the threshold can be greater than or less than two hours. The threshold can be determined based on historic trends indicating how long the user 302 usually sleeps or otherwise stays on the bed 302.

The control circuitry 334 can detect repeated extended sleep events to automatically determine a typical bed time range of the user 308, without requiring the user 308 to enter a bed time range. This can allow the control circuitry 334 to accurately estimate when the user 308 is likely to go to bed for an extended sleep event, regardless of whether the user 308 typically goes to bed using a traditional sleep schedule or a non-traditional sleep schedule. The control circuitry 334 can then use knowledge of the bed time range of the user 308 to control one or more components (including components of the bed 302 and/or non-bed peripherals) based on sensing bed presence during the bed time range or outside of the bed time range.

The control circuitry 334 can automatically determine the bed time range of the user 308 without requiring user inputs. The control circuitry 334 may also determine the bed time range automatically and in combination with user inputs (e.g., using signals sensed by sensors of the bed 302 and/or the central controller). The control circuitry 334 can set the bed time range directly according to user inputs. The control circuitry 334 can associate different bed times with different days of the week. In each of these examples, the control circuitry 334 can control components (e.g., the lighting system 314, thermostat 316, security system 318, oven 322, coffee maker 324, lamp 326, nightlight 328), as a function of sensed bed presence and the bed time range.

The control circuitry 334 can also determine control signals to be transmitted to the thermostat 316 based on user-inputted preferences and/or maintaining improved or preferred sleep quality of the user 308. For example, the control circuitry 334 can determine, based on historic sleep patterns and quality of the user 308 and by applying machine learning models, that the user 308 experiences their best sleep when the bedroom is at 74 degrees. The control circuitry 334 can receive temperature signals from devices and/or sensors in the bedroom indicating a bedroom temperature. When the temperature is below 74 degrees, the control circuitry 334 can determine control signals that cause the thermostat 316 to activate a heating unit to raise the temperature to 74 degrees in the bedroom. When the temperature is above 74 degrees, the control circuitry 334 can determine control signals that cause the thermostat 316 to activate a cooling unit to lower the temperature back to 74 degrees. Sometimes, the control circuitry 334 can determine control signals that cause the thermostat 316 to maintain the bedroom within a temperature range intended to keep the user 308 in particular sleep states and/or transition to next preferred sleep states.

Similarly, the control circuitry 334 can generate control signals to cause heating or cooling elements on the surface of the bed 302 to change temperature at various times, either in response to user interaction with the bed 302, at various pre-programmed times, based on user preference, and/or in response to detecting microclimate temperatures of the user 308 on the bed 302. For example, the control circuitry 334 can activate a heating element to raise the temperature of one side of the surface of the bed 302 to 73 degrees when it is detected that the user 308 has fallen asleep. As another example, upon determining that the user 308 is up for the day, the control circuitry 334 can turn off a heating or cooling element. The user 308 can pre-program various times at which the temperature at the bed surface should be raised or lowered. As another example, temperature sensors on the bed surface can detect microclimates of the user 308. When a detected microclimate drops below a predetermined threshold temperature, the control circuitry 334 can activate a heating element to raise the user 308's body temperature, thereby improving the user 308's comfort, maintaining their sleep cycle, transitioning the user 308 to a next preferred sleep state, and/or maintaining or improving the user 308's sleep quality.

In response to detecting user bed presence and/or that the user 308 is asleep, the control circuitry 334 can also cause the thermostat 316 to change the temperature in different rooms to different values. Other control signals are also possible, and can be based on user preference and user input. Moreover, the control circuitry 334 can receive temperature information from the thermostat 316 and use this information to control functions of the bed 302 or other devices (e.g., adjusting temperatures of heating elements of the bed 302, such as a foot warming pad). The control circuitry 334 may also generate and transmit control signals for controlling other temperature control systems, such as floor heating elements in the bedroom or other rooms.

The control circuitry 334 can communicate with the security system 318, receive information from the security system 318, and generate control signals for controlling functions of the security system 318. For example, in response to detecting that the user 308 in is bed for the evening, the control circuitry 334 can generate control signals to cause the security system 318 to engage or disengage security functions. As another example, the control circuitry 334 can generate and transmit control signals to cause the security system 318 to disable in response to determining that the user 308 is awake for the day (e.g., user 308 is no longer present on the bed 302).

The control circuitry 334 can also receive alerts from the security system 318 and indicate the alert to the user 308. For example, the security system can detect a security breach (e.g., someone opened the door 332 without entering the security code, someone opened a window when the security system 318 is engaged) and communicate the security breach to the control circuitry 334. The control circuitry 334 can then generate control signals to alert the user 308, such as causing the bed 302 to vibrate, causing portions of the bed 302 to articulate (e.g., the head section to raise or lower), causing the lamp 326 to flash on and off at regular intervals, etc. The control circuitry 334 can also alert the user 308 of one bed 302 about a security breach in another bedroom, such as an open window in a kid's bedroom. The control circuitry 334 can send an alert to a garage door controller (e.g., to close and lock the door). The control circuitry 334 can send an alert for the security to be disengaged. The control circuitry 334 can also set off a smart alarm or other alarm device/clock near the bed 302. The control circuitry 334 can transmit a push notification, text message, or other indication of the security breach to the user device 310. Also, the control circuitry 334 can transmit a notification of the security breach to the central controller, which can then determine one or more responses to the security breach.

The control circuitry 334 can additionally generate and transmit control signals for controlling the garage door 320 and receive information indicating a state of the garage door 320 (e.g., open or closed). The control circuitry 334 can also request information on a current state of the garage door 320. If the control circuitry 334 receives a response (e.g., from the garage door opener) that the garage door 320 is open, the control circuitry 334 can notify the user 308 that the garage door is open (e.g., by displaying a notification or other message at the user device 310, outputting a notification at the central controller), and/or generate a control signal to cause the garage door opener to close the door. The control circuitry 334 can also cause the bed 302 to vibrate, cause the lighting system 314 to flash lights in the bedroom, etc. Control signals can also vary depend on the age of the user 308. Similarly, the control circuitry 334 can similarly send and receive communications for controlling or receiving state information associated with the door 332 or the oven 322.

In some implementations, different alerts can be generated for different events. For example, the control circuitry 334 can cause the lamp 326 (or other lights, via the lighting system 314) to flash in a first pattern if the security system 318 has detected a breach, flash in a second pattern if garage door 320 is on, flash in a third pattern if the door 332 is open, flash in a fourth pattern if the oven 322 is on, and flash in a fifth pattern if another bed has detected that a user 308 of that bed has gotten up (e.g., a child has gotten out of bed in the middle of the night as sensed by a sensor in the child's bed). Other examples of alerts include a smoke detector detecting smoke (and communicating this detection to the control circuitry 334), a carbon monoxide tester, a heater malfunctioning, or an alert from another device capable of communicating with the control circuitry 334 and detecting an occurrence to bring to the user 308's attention.

The control circuitry 334 can also communicate with a system or device for controlling a state of the window blinds 330. For example, in response to determining that the user 308 is up for the day or that the user 308 set an alarm to wake up at a particular time, the control circuitry 334 can generate and transmit control signals to cause the window blinds 330 to open. By contrast, if the user 308 gets out of bed prior to a normal rise time for the user 308, the control circuitry 334 can determine that the user 308 is not awake for the day and may not generate control signals that cause the window blinds 330 to open. The control circuitry 334 can also generate and transmit control signals that cause a first set of blinds to close in response to detecting user bed presence and a second set of blinds to close in response to detecting that the user 308 is asleep. As other examples, in response to determining that the user 308 is awake for the day, the control circuitry 334 can generate and transmit control signals to the coffee maker 324 to cause the coffee maker 324 to brew coffee. The control circuitry 334 can generate and transmit control signals to the oven 322 to cause the oven 322 to begin preheating. The control circuitry 334 can use information indicating that the user 308 is awake for the day along with information indicating that the time of year is currently winter and/or that the outside temperature is below a threshold value to generate and transmit control signals to cause a car engine block heater to turn on. The control circuitry 334 can generate and transmit control signals to cause devices to enter a sleep mode in response to detecting user bed presence, or in response to detecting that the user 308 is asleep (e.g., causing a mobile phone of the user 308 to switch into sleep or night mode so that notifications are muted to not disturb the user 308's sleep). Later, upon determining that the user 308 is up for the day, the control circuitry 334 can generate and transmit control signals to cause the mobile phone to switch out of sleep/night mode.

The control circuitry 334 can also communicate with one or more noise control devices. For example, upon determining that the user 308 is in bed for the evening, or that the user 308 is asleep (e.g., based on pressure signals received from the bed 302, audio/decibel signals received from audio sensors positioned on or around the bed 302), the control circuitry 334 can generate and transmit control signals to cause noise cancelation devices to activate. The noise cancelation devices can be part of the bed 302 or located in the bedroom. Upon determining that the user 308 is in bed for the evening or that the user 308 is asleep, the control circuitry 334 can generate and transmit control signals to turn the volume on, off, up, or down, for one or more sound generating devices, such as a stereo system radio, television, computer, tablet, mobile phone, etc.

Additionally, functions of the bed 302 can be controlled by the control circuitry 334 in response to user interactions. For example, the articulation controller can adjust the bed 302 from a flat position to a position in which a head portion of a mattress of the bed 302 is inclined upward (e.g., to facilitate a user sitting up in bed, reading, and/or watching television). Sometimes, the bed 302 includes multiple separately articulable sections. Portions of the bed corresponding to the locations of the air chambers 306a and 306b can be articulated independently from each other, to allow one person to rest in a first position (e.g., a flat position) while a second person rests in a second position (e.g., a reclining position with the head raised at an angle from the waist). Separate positions can be set for two different beds (e.g., two twin beds placed next to each other). The foundation of the bed 302 can include more than one zone that can be independently adjusted. The articulation controller can also provide different levels of massage to one or more users on the bed 302 or cause the bed to vibrate to communicate alerts to the user 308 as described above.

The control circuitry 334 can adjust positions (e.g., incline and decline positions for the user 308 and/or an additional user) in response to user interactions with the bed 302 (e.g., causing the articulation controller to adjust to a first recline position in response to sensing user bed presence). The control circuitry 334 can cause the articulation controller to adjust the bed 302 to a second recline position (e.g., a less reclined, or flat position) in response to determining that the user 308 is asleep. As another example, the control circuitry 334 can receive a communication from the television 312 indicating that the user 308 has turned off the television 312, and in response, the control circuitry 334 can cause the articulation controller to adjust the bed position to a preferred user sleeping position (e.g., due to the user turning off the television 312 while the user 308 is in bed indicating the user 308 wishes to go to sleep).

In some implementations, the control circuitry 334 can control the articulation controller to wake up one user without waking another user of the bed 302. For example, the user 308 and a second user can each set distinct wakeup times (e.g., 6:30 am and 7:15 am respectively). When the wakeup time for the user 308 is reached, the control circuitry 334 can cause the articulation controller to vibrate or change the position of only a side of the bed on which the user 308 is located. When the wakeup time for the second user is reached, the control circuitry 334 can cause the articulation controller to vibrate or change the position of only the side of the bed on which the second user is located. Alternatively, when the second wakeup time occurs, the control circuitry 334 can utilize other methods (such as audio alarms, or turning on the lights) to wake the second user since the user 308 is already awake and therefore will not be disturbed when the control circuitry 334 attempts to wake the second user.

Still referring to FIG. 3, the control circuitry 334 for the bed 302 can utilize information for interactions with the bed 302 by multiple users to generate control signals for controlling functions of various other devices. For example, the control circuitry 334 can wait to generate control signals for devices until both the user 308 and a second user are detected in the bed 302. The control circuitry 334 can generate a first set of control signals to cause the lighting system 314 to turn off a first set of lights upon detecting bed presence of the user 308 and generate a second set of control signals for turning off a second set of lights in response to detecting bed presence of a second user. The control circuitry 334 can also wait until it has been determined that both users are awake for the day before generating control signals to open the window blinds 330. One or more other home automation control signals can be determined and generated by the control circuitry 334, the user device 310, and/or the central controller.

Examples of Data Processing Systems Associated with a Bed

Described are example systems and components for data processing tasks that are, for example, associated with a bed. In some cases, multiple examples of a particular component or group of components are presented. Some examples are redundant and/or mutually exclusive alternatives. Connections between components are shown as examples to illustrate possible network configurations for allowing communication between components. Different formats of connections can be used as technically needed/desired. The connections generally indicate a logical connection that can be created with any technologically feasible format. For example, a network on a motherboard can be created with a printed circuit board, wireless data connections, and/or other types of network connections. Some logical connections are not shown for clarity (e.g., connections with power supplies and/or computer readable memory).

FIG. 4A is a block diagram of an example data processing system 400 that can be associated with a bed system, including those described above (e.g., see FIGS. 1-3). The system 400 includes a pump motherboard 402 and a pump daughterboard 404. The system 400 includes a sensor array 406 having one or more sensors configured to sense physical phenomenon of the environment and/or bed, and to report sensing back to the pump motherboard 402 (e.g., for analysis). The sensor array 406 can include one or more different types of sensors, including but not limited to pressure, temperature, light, movement (e.g., motion), and audio. The system 400 also includes a controller array 408 that can include one or more controllers configured to control logic-controlled devices of the bed and/or environment (e.g., home automation devices, security systems light systems, and other devices described in FIG. 3). The pump motherboard 400 can be in communication with computing devices 414 and cloud services 410 over local networks (e.g., Internet 412) or otherwise as is technically appropriate.

In FIG. 4A, the pump motherboard 402 and daughterboard 404 are communicably coupled. They can be conceptually described as a center or hub of the system 400, with the other components conceptually described as spokes of the system 400. This can mean that each spoke component communicates primarily or exclusively with the pump motherboard 402. For example, a sensor of the sensor array 406 may not be configured to, or may not be able to, communicate directly with a corresponding controller. Instead, the sensor can report a sensor reading to the motherboard 402, and the motherboard 402 can determine that, in response, a controller of the controller array 408 should adjust some parameters of a logic controlled device or otherwise modify a state of one or more peripheral devices.

One advantage of a hub-and-spoke network configuration, or a star-shaped network, is a reduction in network traffic compared to, for example, a mesh network with dynamic routing. If a particular sensor generates a large, continuous stream of traffic, that traffic is transmitted over one spoke to the motherboard 402. The motherboard 402 can marshal and condense that data to a smaller data format for retransmission for storage in a cloud service 410. Additionally or alternatively, the motherboard 402 can generate a single, small, command message to be sent down a different spoke in response to the large stream. For example, if the large stream of data is a pressure reading transmitted from the sensor array 406 a few times a second, the motherboard 402 can respond with a single command message to the controller array 408 to increase the pressure in an air chamber of the bed. In this case, the single command message can be orders of magnitude smaller than the stream of pressure readings.

As another advantage, a hub-and-spoke network configuration can allow for an extensible network that accommodates components being added, removed, failing, etc. This can allow more, fewer, or different sensors in the sensor array 406, controllers in the controller array 408, computing devices 414, and/or cloud services 410. For example, if a particular sensor fails or is deprecated by a newer version, the system 400 can be configured such that only the motherboard 402 needs to be updated about the replacement sensor. This can allow product differentiation where the same motherboard 402 can support an entry level product with fewer sensors and controllers, a higher value product with more sensors and controllers, and customer personalization where a customer can add their own selected components to the system 400.

Additionally, a line of air bed products can use the system 400 with different components. In an application in which every air bed in the product line includes both a central logic unit and a pump, the motherboard 402 (and optionally the daughterboard 404) can be designed to fit within a single, universal housing. For each upgrade of the product in the product line, additional sensors, controllers, cloud services, etc., can be added. Design, manufacturing, and testing time can be reduced by designing all products in a product line from this base, compared to a product line in which each product has a bespoke logic control system.

Each of the components discussed above can be realized in a wide variety of technologies and configurations. Below, some examples of each component are discussed. Sometimes, two or more components of the system 400 can be realized in a single alternative component; some components can be realized in multiple, separate components; and/or some functionality can be provided by different components.

FIG. 4B is a block diagram showing communication paths of the system 400. As described, the motherboard 402 and daughterboard 404 may act as a hub of the system 400. When the pump daughterboard 404 communicates with cloud services 410 or other components, communications may be routed through the motherboard 402. This may allow the bed to have a single connection with the Internet 412. The computing device 414 may also have a connection to the Internet 412, possibly through the same gateway used by the bed and/or a different gateway (e.g., a cell service provider).

In FIG. 4B, cloud services 410d and 410e may be configured such that the motherboard 402 communicates with the cloud service directly (e.g., without having to use another cloud service 410 as an intermediary). Additionally or alternatively, some cloud services 410 (e.g., 410f) may only be reachable by the motherboard 402 through an intermediary cloud service (e.g., 410e). While not shown here, some cloud services 410 may be reachable either directly or indirectly by the pump motherboard 402.

Additionally, some or all of the cloud services 410 may communicate with other cloud services, including the transfer of data and/or remote function calls according to any technologically appropriate format. For example, one cloud service 410 may request a copy for another cloud service's 410 data (e.g., for purposes of backup, coordination, migration, calculations, data mining). Many cloud services 410 may also contain data that is indexed according to specific users tracked by the user account cloud 410c and/or the bed data cloud 410a. These cloud services 410 may communicate with the user account cloud 410c and/or the bed data cloud 410a when accessing data specific to a particular user or bed.

FIG. 5 is a block diagram of an example motherboard 402 in a data processing system associated with a bed system (e.g., refer to FIGS. 1-3). In this example, compared to other examples described below, this motherboard 402 consists of relatively fewer parts and can be limited to provide a relatively limited feature set.

The motherboard 402 includes a power supply 500, a processor 502, and computer memory 512. In general, the power supply 500 includes hardware used to receive electrical power from an outside source and supply it to components of the motherboard 402. The power supply may include a battery pack and/or wall outlet adapter, an AC to DC converter, a DC to AC converter, a power conditioner, a capacitor bank, and/or one or more interfaces for providing power in the current type, voltage, etc., needed by other components of the motherboard 402.

The processor 502 is generally a device for receiving input, performing logical determinations, and providing output. The processor 502 can be a central processing unit, a microprocessor, general purpose logic circuitry, application-specific integrated circuitry, a combination of these, and/or other hardware.

The memory 512 is generally one or more devices for storing data, which may include long term stable data storage (e.g., on a hard disk), short term unstable (e.g., on Random Access Memory), or any other technologically appropriate configuration.

The motherboard 402 includes a pump controller 504 and a pump motor 506. The pump controller 504 can receive commands from the processor 502 to control functioning of the pump motor 506. For example, the pump controller 504 can receive a command to increase pressure of an air chamber by 0.3 pounds per square inch (PSI). The pump controller 504, in response, engages a valve so that the pump motor 506 pumps air into the selected air chamber, and can engage the pump motor 506 for a length of time that corresponds to 0.3 PSI or until a sensor indicates that pressure has been increased by 0.3 PSI. Sometimes, the message can specify that the chamber should be inflated to a target PSI, and the pump controller 504 can engage the pump motor 506 until the target PSI is reached.

A valve solenoid 508 can control which air chamber a pump is connected to. In some cases, the solenoid 508 can be controlled by the processor 502 directly. In some cases, the solenoid 508 can be controlled by the pump controller 504.

A remote interface 510 of the motherboard 402 can allow the motherboard 402 to communicate with other components of a data processing system. For example, the motherboard 402 can be able to communicate with one or more daughterboards, with peripheral sensors, and/or with peripheral controllers through the remote interface 510. The remote interface 510 can provide any technologically appropriate communication interface, including but not limited to multiple communication interfaces such as WiFi, Bluetooth, and copper wired networks.

FIG. 6 is a block diagram of another example motherboard 402. Compared to the motherboard 402 in FIG. 5, the motherboard 402 in FIG. 6 can contain more components and provide more functionality in some applications.

This motherboard 402 can further include a valve controller 600, a pressure sensor 602, a universal serial bus (USB) stack 604, a WiFi radio 606, a Bluetooth Low Energy (BLE) radio 608, a ZigBee radio 610, a Bluetooth radio 612, and a computer memory 512.

The valve controller 600 can convert commands from the processor 502 into control signals for the valve solenoid 508. For example, the processor 502 can issue a command to the valve controller 600 to connect the pump to a particular air chamber out of a group of air chambers in an air bed. The valve controller 600 can control the position of the valve solenoid 508 so the pump is connected to the indicated air chamber.

The pressure sensor 602 can read pressure readings from one or more air chambers of the air bed. The pressure sensor 602 can also preform digital sensor conditioning. As described herein, multiple pressure sensors 602 can be included as part of the motherboard 402 or otherwise in communication with the motherboard 402.

The motherboard 402 can include a suite of network interfaces 604, 606, 608, 610, 612, etc., including but not limited to those shown in FIG. 6. These network interfaces can allow the motherboard to communicate over a wired or wireless network with any devices, including but not limited to peripheral sensors, peripheral controllers, computing devices, and devices and services connected to the Internet 412.

FIG. 7 is a block diagram of an example daughterboard 404 used in a data processing system associated with a bed system described herein. One or more daughterboards 404 can be connected to the motherboard 402. Some daughterboards 404 can be designed to offload particular and/or compartmentalized tasks from the motherboard 402. This can be advantageous if the particular tasks are computationally intensive, proprietary, or subject to future revisions. For example, the daughterboard 404 can be used to calculate a particular sleep data metric. This metric can be computationally intensive, and calculating the metric on the daughterboard 404 can free up resources of the motherboard 402 while the metric is calculated. The sleep metric may be subject to future revisions. To update the system 400 with the new metric, it is possible that only the daughterboard 404 calculates the metric to be replaced. In this case, the same motherboard 402 and other components can be used, saving the need to perform unit testing of additional components instead of just the daughterboard 404.

The daughterboard 404 includes a power supply 700, a processor 702, computer readable memory 704, a pressure sensor 706, and a WiFi radio 708. The processor 702 can use the pressure sensor 706 to gather information about pressure of air bed chambers. The processor 702 can perform an algorithm to calculate a sleep metric (e.g., sleep quality, bed presence, whether the user fell asleep, a heartrate, a respiration rate, movement, etc.). Sometimes, the sleep metric can be calculated from only air chamber pressure. The sleep metric can also be calculated using signals from a variety of sensors (e.g., movement, pressure, temperature, and/or audio sensors). The processor 702 can receive that data from sensors that may be internal to the daughterboard 404, accessible via the WiFi radio 708, or otherwise in communication with the processor 702. Once the sleep metric is calculated, the processor 702 can report that sleep metric to, for example, the motherboard 402. The motherboard 402 can generate instructions for outputting the sleep metric to the user or using the sleep metric to determine other user information or controls to control the bed and/or peripheral devices.

FIG. 8 is a block diagram of an example motherboard 800 with no daughterboard used in a data processing system associated with a bed system. In this example, the motherboard 800 can perform most, all, or more of the features described with reference to the motherboard 402 in FIG. 6 and the daughterboard 404 in FIG. 7.

FIG. 9A is a block diagram of an example sensory array 406 used in a data processing system associated with a bed system described herein. The sensor array 406 is a conceptual grouping of some or all peripheral sensors that communicate with the motherboard 402 but are not native to the motherboard 402. The peripheral sensors 902, 904, 906, 908, 910, etc. of the sensor array 406 communicate with the motherboard 402 through one or more network interfaces 604, 606, 608, 610, and 612 of the motherboard, as is appropriate for the configuration of the particular sensor. For example, a sensor that outputs a reading over a USB cable can communicate through the USB stack 604.

Some peripheral sensors of the sensor array 406 can be bed mounted sensors 900 (e.g., temperature sensor 906, light sensor 908, sound sensor 910). The bed mounted sensors 900 can be embedded into a bed structure and sold with the bed, or later affixed to the structure (e.g., part of a pressure sensing pad that is removably installed on a top surface of the bed, part of a temperature sensing or heating pad that is removably installed on the top surface of the bed, integrated into the top surface, attached along connecting tubes between a pump and air chambers, within air chambers, attached to a headboard, attached to one or more regions of an adjustable foundation). One or more of the sensors 902 can be load cells or force sensors as described in FIG. 9C. Other sensors 902 and 904 may not be mounted to the bed and can include a pressure sensor 902 and/or peripheral sensor 904. For example, the sensors 902 and 904 can be integrated or otherwise part of a user mobile device (e.g., mobile phone, wearable device). The sensors 902 and 904 can also be part of a central controller for controlling the bed and peripheral devices. Sometimes, the sensors 902 and 904 can be part of one or more home automation devices or other peripheral devices. In some implementations, the peripheral sensors 904 can include but are not limited to light-detection-and-ranging (LiDAR), radar, and/or time-of-flight (ToF) sensors. LiDAR sensors can, for example emit light from a laser in order to collect measurements, including but not limited to user movement and/or user biometrics. The light can be emitted from pulsed laser beams with wavelengths in a near-infrared (NIR) range. Radar sensors can use radio waves and/or microwaves and thus operate at longer wavelengths than LiDAR sensors. Radar sensors can similarly be used to detect user movement and/or user biometrics. ToF sensors can be used to determine amounts of time that it takes photons or other energy particles to travel between two points, which can be similarly used to detect user movement and/or user biometrics. One or more other peripheral sensors 904 are also possible.

Sometimes, some or all of the bed mounted sensors 900 and/or sensors 902 and 904 share networking hardware (e.g., a conduit that contains wires from each sensor, a multi-wire cable or plug that, when affixed to the motherboard 402, connect all the associated sensors with the motherboard 402). One, some, or all the sensors 902, 904, 906, 908, and 910 can sense features of a mattress (e.g., pressure, temperature, light, sound, and/or other features) and features external to the mattress. Sometimes, pressure sensor 902 can sense pressure of the mattress while some or all the sensors 902, 904, 906, 908, and 910 sense features of the mattress and/or features external to the mattress.

FIG. 9B is a schematic top view of a bed 920 having a sensor strip 932 with sensors 934A-N used in a data processing system associated with the bed 920. The bed 920 includes a mattress 922 (e.g., refer to FIG. 1). The mattress 922 can have a foam tub 930 beneath a top of the mattress 922. The foam tub 930 can have air chamber 923A and/or 923B, similar to those described herein.

The sensor strip 932 can be attached across the mattress top 924 from one lateral side to an opposing lateral side (e.g., from left to right). The sensor strip 932 can be attached proximate to a head section of the mattress 922 to measure temperature and/or humidity values around a chest area of a user 936. The sensor strip 932 can also be placed at a center point (e.g., midpoint) of the mattress 922 such that the distances 938 and 940 are equal to each other. The sensor strip 932 can be placed at other locations to capture temperature and/or humidity values at the top of the mattress 922.

The sensors 934A-N can be any one or more of the temperature sensors 906 described in FIG. 9A. The sensor strip 932 can also include a carrier strip 933 having a first strip portion 933A and a second strip portion 933B. The carrier strip 933 can be releasably attached to the foam tub layer 920 and extend between the opposite lateral ends of the foam tub 920. The sensor strip 932 can have first sensors 934A-N and second sensors 934A-N. Each of the first and second sensors 934A-N can have five sensors each. For example, a sensor strip 932 for a king or queen size mattress can have a total of ten sensors. As may be appreciated, any suitable number of sensors may be used. When the user 936 is positioned on top of the mattress 922 over the air chamber 923A, the first sensors 934A-N can measure temperature and/or humidity of the mattress top 924 above the air chamber 923A. Those values can be used to, for example, determine a conditioned airflow to supply to the air chamber 923A. Temperature and/or humidity values measured by the second sensors 934A-N can be used to, for example, determine a conditioned airflow to supply to the air chamber 923B. The bed system 920 can provide for custom airflow to different portions of the mattress 922 based on body temperatures of users and/or temperatures of different portions of the mattress top 924.

Sometimes, two or more separate sensor strips can be attached to the mattress 922 (e.g., a first sensor strip over the air chamber 923A and a second sensor strip, separate from the first sensor strip, over the air chamber 923B). The first and second sensor strips can be attached to a center of the mattress top 924 via fastening elements, such as adhesive. The sensor strip 932 can also be easily replaced with another sensor strip.

FIG. 9C is a schematic diagram of an example bed with force sensors 955 located at the bottom of legs 953 of the bed (e.g., in four, six, eight, or another number of legs). The force sensors 955 may also be located elsewhere on the bed with similar effect (e.g., between the legs 953 and platform 950). When a strain gauge is used as the force sensors 955, the force sensor(s) 955 can be positioned nearer centers of the legs 953. The force sensors 955 can be load cells.

FIG. 10 is a block diagram of an example controller array 408 used in a data processing system associated with a bed system. The controller array 408 is a conceptual grouping of some or all peripheral controllers that communicate with the motherboard 402 but are not native to the motherboard 402. The peripheral controllers can communicate with the motherboard 402 through one or more of the network interfaces 604, 606, 608, 610, and 612 of the motherboard, as is appropriate for the configuration of the particular controller. Some of the controllers can be bed mounted controllers 1000, such as a temperature controller 1006, a light controller 1008, and a speaker controller 1010, as described in reference to bed-mounted sensors in FIG. 9A. Peripheral controllers 1002 and 1004 can be in communication with the motherboard 402, but optionally not mounted to the bed.

FIG. 11 is a block diagram of an example computing device 412 used in a data processing system associated with a bed system. The computing device 412 can include computing devices used by a user of a bed including but not limited to mobile computing devices (e.g., mobile phones, tablet computers, laptops, smart phones, wearable devices), desktop computers, home automation devices, and/or central controllers or other hub devices.

The computing device 412 includes a power supply 1100, a processor 1102, and computer readable memory 1104. User input and output can be transmitted by speakers 1106, a touchscreen 1108, or other not shown components (e.g., a pointing device or keyboard). The computing device 412 can run applications 1110 including, for example, applications to allow the user to interact with the system 400. These applications can allow a user to view information about the bed (e.g., sensor readings, sleep metrics), information about themselves (e.g., health conditions detected based on signals sensed at the bed), and/or configure the system 400 behavior (e.g., set desired firmness, set desired behavior for peripheral devices). The computing device 412 can be used in addition to, or to replace, the remote control 122 described above.

FIG. 12 is a block diagram of an example bed data cloud service 410a used in a data processing system associated with a bed system. Here, the bed data cloud service 410a is configured to collect sensor data and sleep data from a particular bed, and to match the data with one or more users that used the bed when the data was generated.

The bed data cloud service 410a includes a network interface 1200, a communication manager 1202, server hardware 1204, and server system software 1206. The bed data cloud service 410a is also shown with a user identification module 1208, a device management 1210 module, a sensor data module 1210, and an advanced sleep data module 1214. The network interface 1200 includes hardware and low level software to allow hardware devices (e.g., components of the service 410a) to communicate over networks (e.g., with each other, with other destinations over the Internet 412). The network interface 1200 can include network cards, routers, modems, and other hardware.

The communication manager 1202 generally includes hardware and software that operate above the network interface 1200 such as software to initiate, maintain, and tear down network communications used by the service 410a (e.g., TCP/IP, SSL or TLS, Torrent, and other communication sessions over local or wide area networks). The communication manager 1202 can also provide load balancing and other services to other elements of the service 410a. The server hardware 1204 generally includes physical processing devices used to instantiate and maintain the service 410a. This hardware includes, but is not limited to, processors (e.g., central processing units, ASICs, graphical processers) and computer readable memory (e.g., random access memory, stable hard disks, tape backup). One or more servers can be configured into clusters, multi-computer, or datacenters that can be geographically separate or connected. The server system software 1206 generally includes software that runs on the server hardware 1204 to provide operating environments to applications and services (e.g., operating systems running on real servers, virtual machines instantiated on real servers to create many virtual servers, server level operations such as data migration, redundancy, and backup).

The user identification 1208 can include, or reference, data related to users of beds with associated data processing systems. The users may include customers, owners, or other users registered with the service 410a or another service. Each user can have a unique identifier, user credentials, contact information, billing information, demographic information, or any other technologically appropriate information.

The device manager 1210 can include, or reference, data related to beds or other products associated with data processing systems. The beds can include products sold or registered with a system associated with the service 410a. Each bed can have a unique identifier, model and/or serial number, sales information, geographic information, delivery information, a listing of associated sensors and control peripherals, etc. An index or indexes stored by the service 410a can identify users associated with beds. This index can record sales of a bed to a user, users that sleep in a bed, etc.

The sensor data 1212 can record raw or condensed sensor data recorded by beds with associated data processing systems. For example, a bed's data processing system can have temperature, pressure, motion, audio, and/or light sensors. Readings from these sensors, either in raw form or in a format generated from the raw data (e.g. sleep metrics), can be communicated by the bed's data processing system to the service 410a for storage in the sensor data 1212. An index or indexes stored by the service 410a can identify users and/or beds associated with the sensor data 1212.

The service 410a can use any of its available data (e.g., sensor data 1212) to generate advanced sleep data 1214. The advanced sleep data 1214 includes sleep metrics and other data generated from sensor readings (e.g., health information). Some of these calculations can be performed in the service 410a instead of locally on the bed's data processing system because the calculations can be computationally complex or require a large amount of memory space or processor power that may not be available on the bed's data processing system. This can help allow a bed system to operate with a relatively simple controller while being part of a system that performs relatively complex tasks and computations. However, other configurations are possible in which the service 410a is executed on the bed system. For example, the pump motherboard 402 and/or pump daughterboard 404 can contain sufficient processor and memory resources to execute the service 410a. In some cases, this can allow the service 410a to be executed redundantly, to protect against loss of network.

For example, the service 410a can retrieve one or more machine learning models from a remote data store and use those models to determine the advanced sleep data 1214. The service 410a can retrieve one or more models to determine overall sleep quality of the user based on currently detected sensor data 1212 and/or historic sensor data. The service 410a can retrieve other models to determine whether the user is snoring based on the detected sensor data 1212. The service 410a can retrieve other models to determine whether the user experiences a health condition based on the data 1212.

FIG. 13 is a block diagram of an example sleep data cloud service 410b used in a data processing system associated with a bed system. Here, the sleep data cloud service 410b is configured to record data related to users' sleep experience. The service 410b includes a network interface 1300, a communication manager 1302, server hardware 1304, and server system software 1306. The service 410b also includes a user identification module 1308, a pressure sensor manager 1310, a pressure based sleep data module 1312, a raw pressure sensor data module 1314, and a non-pressure sleep data module 1316. Sometimes, the service 410b can include a sensor manager for each sensor. The service 410b can also include a sensor manager that relates to multiple sensors in beds (e.g., a single sensor manager can relate to pressure, temperature, light, movement, and audio sensors in a bed).

The bed sensor manager 1310 can include, or reference, data related to the configuration and operation of sensors in beds such as pressure sensors, force sensors, or other sensors of a bed. This data can include an identifier of the types of sensors in a particular bed, their settings and calibration data, etc. The bed based sleep data 1312 can use raw bed sensor data 1314 to calculate sleep metrics tied to bed sensor data. For example, user presence, movements, weight change, heartrate, and breathing rate can be determined from raw bed sensor data 1314. An index or indexes stored by the service 410b can identify users associated with pressure sensors, raw pressure sensor data, and/or pressure based sleep data. The non-bed sleep data 1316 can use other sources of data to calculate sleep metrics. User-entered preferences, light sensor readings, and sound sensor readings can be used to track sleep data. User presence can also be determined from a combination of raw bed sensor data 1314 and non-bed sleep data 1316 (e.g., raw temperature data gathered from a peripheral device on a nightstand by the bed). Sometimes, bed presence can be determined using only the temperature data. Changes in temperature data can be monitored to determine bed presence or absence in a temporal interval (e.g., window of time) of a given duration. The temperature and/or pressure data can also be combined with other sensing modalities or motion sensors that reflect different forms of movement (e.g., load cells) to accurately detect user presence. Sometimes, bed presence can be determined using only the load cell data. In other instances, data from two or more sensors can be used to determine bed presence. For example, the temperature and/or pressure data can be provided as input to a bed presence classifier, which can determine user bed presence based on real-time or near real-time data collected at the bed. The classifier can be trained to differentiate the temperature data from the pressure data, identify peak values in the temperature and pressure data, and generate a bed presence indication based on correlating the peak values. The peak values can be within a threshold distance from each other to then generate an indication that the user is in the bed. An index or indexes stored by the service 410b can identify users associated with sensors and/or the data 1316.

FIG. 14 is a block diagram of an example user account cloud service 410c used in a data processing system associated with a bed system. Here, the service 410c is configured to record a list of users and to identify other data related to those users. The service 410c includes a network interface 1400, a communication manager 1402, server hardware 1404, and server system software 1406. The service 410c also includes a user identification module 1408, a purchase history module 1410, an engagement module 1412, and an application usage history module 1414.

The user identification module 1408 can include, or reference, data related to users of beds with associated data processing systems, as described above. The purchase history module 1410 can include, or reference, data related to purchases by users. The purchase data can include a sale's contact information, billing information, and salesperson information associated with the user's purchase of the bed system. An index or indexes stored by the service 410c can identify users associated with a bed purchase.

The engagement module 1412 can track user interactions with the manufacturer, vendor, and/or manager of the bed/cloud services. This data can include communications (e.g., emails, service calls), data from sales (e.g., sales receipts, configuration logs), and social network interactions. The data can also include servicing, maintenance, or replacements of components of the user's bed system. The usage history module 1414 can contain data about user interactions with applications and/or remote controls of the bed. A monitoring and configuration application can be distributed to run on, for example, computing devices 412 described herein. The application can log and report user interactions for storage in the application usage history module 1414. An index or indexes stored by the service 410c can also identify users associated with each log entry. User interactions stored in the module 1414 can optionally be used to determine or predict user preferences and/or settings for the user's bed and/or peripheral devices that can improve the user's overall sleep quality.

FIG. 15 is a block diagram of an example point of sale cloud service 1500 used in a data processing system associated with a bed system. Here, the service 1500 can record data related to users' purchases, specifically purchases of bed systems described herein. The service 1500 is shown with a network interface 1502, a communication manager 1504, server hardware 1506, and server system software 1508. The service 1500 also includes a user identification module 1510, a purchase history module 1512, and a bed setup module 1514.

The purchase history module 1512 can include, or reference, data related to purchases made by users identified in the module 1510, such as data of a sale, price, and location of sale, delivery address, and configuration options selected by the users at the time of sale. The configuration options can include selections made by the user about how they wish their newly purchased beds to be setup and can include expected sleep schedule, a listing of peripheral sensors and controllers that they have or will install, etc.

The bed setup module 1514 can include, or reference, data related to installations of beds that users purchase. The bed setup data can include a date and address to which a bed is delivered, a person who accepts delivery, configuration that is applied to the bed upon delivery (e.g., firmness settings), name(s) of bed user(s), which side of the bed each user will use, etc. Data recorded in the service 1500 can be referenced by a user's bed system at later times to control functionality of the bed system and/or to send control signals to peripheral components. This can allow a salesperson to collect information from the user at the point of sale that later facilitates bed system automation. Sometimes, some or all aspects of the bed system can be automated with little or no user-entered data required after the point of sale. Sometimes, data recorded in the service 1500 can be used in connection with other, user-entered data.

FIG. 16 is a block diagram of an example environment cloud service 1600 used in a data processing system associated with a bed system. Here, the service 1600 is configured to record data related to users' home environment. The service 1600 includes a network interface 1602, a communication manager 1604, server hardware 1606, and server system software 1608. The service 1600 also includes a user identification module 1610, an environmental sensors module 1612, and an environmental factors module 1614. The environmental sensors module 1612 can include a listing and identification of sensors that users identified in the module 1610 to have installed in and/or surrounding their bed (e.g., light, noise/audio, vibration, thermostats, movement/motion sensors). The module 1612 can also store historical readings or reports from the environmental sensors. The module 1612 can be accessed at a later time and used by one or more cloud services described herein to determine sleep quality and/or health information of the users. The environmental factors module 1614 can include reports generated based on data in the module 1612. For example, the module 1614 can generate and retain a report indicating frequency and duration of instances of increased lighting when the user is asleep based on light sensor data that is stored in the environment sensors module 1612.

In the examples discussed here, each cloud service 410 is shown with some of the same components. These same components can be partially or wholly shared between services, or they can be separate. Sometimes, each service can have separate copies of some or all the components that are the same or different in some ways. These components are provided as illustrative examples. In other examples, each cloud service can have different number, types, and styles of components that are technically possible.

FIG. 17 is a block diagram of an example of using a data processing system associated with a bed to automate peripherals around the bed. Shown here is a behavior analysis module 1700 that runs on the motherboard 402. The behavior analysis module 1700 can be one or more software components stored on the computer memory 512 and executed by the processor 502. In general, the module 1700 can collect data from a variety of sources (e.g., sensors 902, 904, 906, 908, and/or 910, non-sensor local sources 1704, cloud data services 410a and/or 410c) and use a behavioral algorithm 1702 (e.g., machine learning model(s)) to generate actions to be taken (e.g., commands to send to peripheral controllers, data to send to cloud services, such as the bed data cloud 410a and/or the user account cloud 410c). This can be useful, for example, in tracking user behavior and automating devices in communication with the user's bed.

The module 1700 can collect data from any technologically appropriate source (e.g., sensors of the sensor array 406) to gather data about features of a bed, the bed's environment, and/or the bed's users. The data can provide the module 1700 with information about a current state of the bed's environment. For example, the module 1700 can access readings from the pressure sensor 902 to determine air chamber pressure in the bed. From this reading, and potentially other data, user presence can be determined. In another example, the module 1700 can access the light sensor 908 to detect the amount of light in the environment. The module 1700 can also access the temperature sensor 906 to detect a temperature in the environment and/or microclimates in the bed. Using this data, the module 1700 can determine whether temperature adjustments should be made to the environment and/or components of the bed to improve the user's sleep quality and overall comfort. Similarly, the module 1700 can access data from cloud services to make more accurate determinations of user sleep quality, health information, and/or control the bed and/or peripheral devices. For example, the behavior analysis module 1700 can access the bed cloud service 410a to access historical sensor data 1212 and/or advanced sleep data 1214. The module 1700 can also access a weather reporting service, a 3rd party data provider (e.g., traffic and news data, emergency broadcast data, user travel data), and/or a clock and calendar service. Using data retrieved from the cloud services 410, the module 1700 can accurately determine user sleep quality, health information, and/or control of the bed and/or peripheral devices. Similarly, the module 1700 can access data from non-sensor sources 1704, such as a local clock and calendar service (e.g., a component of the motherboard 402 or of the processor 502). The module 1700 can use this information to determine, for example, times of day that the user is in bed, asleep, waking up, and/or going to bed.

The behavior analysis module 1700 can aggregate and prepare this data for use with one or more behavioral algorithms 1702 (e.g., machine learning models). The behavioral algorithms 1702 can be used to learn a user's behavior and/or to perform some action based on the state of the accessed data and/or the predicted user behavior. For example, the behavior algorithm 1702 can use available data (e.g., pressure sensor, non-sensor data, clock and calendar data) to create a model of when a user goes to bed every night. Later, the same or a different behavioral algorithm 1702 can be used to determine if an increase in air chamber pressure is likely to indicate a user going to bed and, if so, send some data to a third-party cloud service 410 and/or engage a peripheral controller 1002 or 1004, foundation actuators 1006, a temperature controller 1008, and/or an under-bed lighting controller 1010.

Data described in this document can be organized into time periods that align with user behavior. For example, sensor data used as training data and for other purposes can be indexed by an associated sleep session. In some cases, sleep sessions are a period of time in which a user intents to, and does, sleep on the bed. For example, a user may go to bed at 10:00 PM on Monday, and awaken at 6:00 AM the next Tuesday by their alarm. In this case, a sleep session may be identified for this. The sleep session may be started when the user enters the bed (e.g., at 10:00 PM), when the user falls asleep (e.g., at 10:17 PM) as determined from sensor data, or at another time (e.g., Noon on Monday for a 24 hour sleep session). The sleep session may be ended when the user awakens (e.g., 6:00 AM), exits the bed (e.g., at 6:03 AM), or at another time (e.g., Noon on Tuesday for a 24 hour sleep session). As will be appreciated, many sleep sessions occur at night, spanning across two calendar days. However, other types of sleep sessions are possible. For example, a user that works an overnight shift, e.g., sleep from about Noon to about 8:00 PM every day, and thus their sleep session would be contained within a single calendar day. The particular delineations of the sleep sessions for a single user or a class of users can be identified based on user input (e.g., entering into a GUI their own sleep habits), automatically identified (e.g., without user input), or via another technologically appropriate process.

Here, the module 1700 and the behavioral algorithm 1702 are shown as components of the motherboard 402. Other configurations are also possible. For example, the same or a similar behavioral analysis module 1700 and/or behavioral algorithm 1702 can be run in one or more cloud services, and resulting output can be sent to the pump motherboard 402, a controller in the controller array 408, or to any other technologically appropriate recipient described throughout this document.

FIG. 18 shows an example of a computing device 1800 and an example of a mobile computing device that can be used to implement the techniques described here. The computing device 1800 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.

The computing device 1800 includes a processor 1802, a memory 1804, a storage device 1806, a high-speed interface 1808 connecting to the memory 1804 and multiple high-speed expansion ports 1810, and a low-speed interface 1812 connecting to a low-speed expansion port 1814 and the storage device 1806. Each of the processor 1802, the memory 1804, the storage device 1806, the high-speed interface 1808, the high-speed expansion ports 1810, and the low-speed interface 1812, are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. The processor 1802 can process instructions for execution within the computing device 1800, including instructions stored in the memory 1804 or on the storage device 1806 to display graphical information for a GUI on an external input/output device, such as a display 1816 coupled to the high-speed interface 1808. In other implementations, multiple processors and/or multiple buses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices can be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). The memory 1804 stores information within the computing device 1800. In some implementations, the memory 1804 is a volatile memory unit or units. In some implementations, the memory 1804 is a non-volatile memory unit or units. The memory 1804 can also be another form of computer-readable medium, such as a magnetic or optical disk. The storage device 1806 is capable of providing mass storage for the computing device 1800. In some implementations, the storage device 1806 can be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product can also contain instructions that, when executed, perform one or more methods, such as those described above. The computer program product can also be tangibly embodied in a computer- or machine-readable medium, such as the memory 1804, the storage device 1806, or memory on the processor 1802.

The high-speed interface 1808 manages bandwidth-intensive operations for the computing device 1800, while the low-speed interface 1812 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In some implementations, the high-speed interface 1808 is coupled to the memory 1804, the display 1816 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 1810, which can accept various expansion cards (not shown). In the implementation, the low-speed interface 1812 is coupled to the storage device 1806 and the low-speed expansion port 1814. The low-speed expansion port 1814, which can include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) can be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter. The computing device 1800 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 1820, or multiple times in a group of such servers. In addition, it can be implemented in a personal computer such as a laptop computer 1822. It can also be implemented as part of a rack server system 1824. Alternatively, components from the computing device 1800 can be combined with other components in a mobile device (not shown), such as a mobile computing device 1850. Each of such devices can contain one or more of the computing device 1800 and the mobile computing device 1850, and an entire system can be made up of multiple computing devices communicating with each other. The mobile computing device 1850 includes a processor 1852, a memory 1864, an input/output device such as a display 1854, a communication interface 1866, and a transceiver 1868, among other components. The mobile computing device 1850 can also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the processor 1852, the memory 1864, the display 1854, the communication interface 1866, and the transceiver 1868, are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.

The processor 1852 can execute instructions within the mobile computing device 1850, including instructions stored in the memory 1864. The processor 1852 can be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor 1852 can provide, for example, for coordination of the other components of the mobile computing device 1850, such as control of user interfaces, applications run by the mobile computing device 1850, and wireless communication by the mobile computing device 1850. The processor 1852 can communicate with a user through a control interface 1858 and a display interface 1856 coupled to the display 1854. The display 1854 can be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 1856 can comprise appropriate circuitry for driving the display 1854 to present graphical and other information to a user. The control interface 1858 can receive commands from a user and convert them for submission to the processor 1852. In addition, an external interface 1862 can provide communication with the processor 1852, so as to enable near area communication of the mobile computing device 1850 with other devices. The external interface 1862 can provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces can also be used.

The memory 1864 stores information within the mobile computing device 1850. The memory 1864 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 1874 can also be provided and connected to the mobile computing device 1850 through an expansion interface 1872, which can include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 1874 can provide extra storage space for the mobile computing device 1850, or can also store applications or other information for the mobile computing device 1850. Specifically, the expansion memory 1874 can include instructions to carry out or supplement the processes described above, and can include secure information also. Thus, for example, the expansion memory 1874 can be provide as a security module for the mobile computing device 1850, and can be programmed with instructions that permit secure use of the mobile computing device 1850. In addition, secure applications can be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

The memory can include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The computer program product can be a computer- or machine-readable medium, such as the memory 1864, the expansion memory 1874, or memory on the processor 1852. In some implementations, the computer program product can be received in a propagated signal, for example, over the transceiver 1868 or the external interface 1862.

The mobile computing device 1850 can communicate wirelessly through the communication interface 1866, which can include digital signal processing circuitry where necessary. The communication interface 1866 can provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication can occur, for example, through the transceiver 1868 using a radio-frequency. In addition, short-range communication can occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 1870 can provide additional navigation- and location-related wireless data to the mobile computing device 1850, which can be used as appropriate by applications running on the mobile computing device 1850. The mobile computing device 1850 can also communicate audibly using an audio codec 1860, which can receive spoken information from a user and convert it to usable digital information. The audio codec 1860 can likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 1850. Such sound can include sound from voice telephone calls, can include recorded sound (e.g., voice messages, music files, etc.) and can also include sound generated by applications operating on the mobile computing device 1850. The mobile computing device 1850 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a cellular telephone 1880. It can also be implemented as part of a smart-phone 1882, personal digital assistant, or other similar mobile device.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input. The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet. The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

FIG. 19 is a schematic of the layout and positioning of temperature sensors within a smart bed. FIG. 19 depicts an example of a Queen-sized bed with dimensions of 152.4 cm in width and 203.2 cm in length. The bed includes temperature sensors arranged along a temperature sensor strip (TSS), as well as a pressure sensor embedded within an inflatable air bladder, both of which contribute to the overall data collection related to sleep patterns. The temperature sensors are arranged along a sensor strip, with individual sensors labeled T1 through T5, starting from the edge of the bed toward the center. These sensors can be strategically placed to monitor skin temperature, which is related to core body temperature (CBT) and plays a role in sleep regulation. High daytime CBT and low skin temperature support vigilance, while low nighttime CBT and high skin temperature promote sleep. Managing these temperature changes can influence sleep initiation, maintenance, and the experience of waking up refreshed. In some examples, the temperature sensors can be positioned within a King-sized bed or any other sized bed. In some of these examples, the positioning of the temperature sensors (e.g., spacing) can be proportionally scaled to fit the, e.g., King-sized bed, taking as reference the Queen-sized bed depicted in FIG. 19.

For example, peripheral vasodilation, which increases blood flow to the skin, plays a role in the decline of CBT during sleep by allowing internal heat to dissipate into the surrounding environment. The temperature sensors in FIG. 19 are positioned 0.25 inches below the bed's surface along a TSS, with a spacing of 14 cm between each sensor in the Queen-sized bed depicted. As may be appreciated, the temperature sensors may be positioned on the bed surface and/or at any suitable depth and the temperature sensors may be spaced any suitable distance. This sensor arrangement is configured to capture measurements of skin temperature, which typically rises during the transition from wakefulness to sleep. In some embodiments, distal (hands and feet) skin temperatures may increase by approximately 1° C., while proximal (abdomen and chest) skin temperatures can increase by approximately 0.5° C. during this transition.

The sensing technology depicted in FIG. 19 can, in some embodiments, use a pressure signal sampled at 500 Hz from a sensor embedded within an inflatable air bladder inside the smart bed. As may be appreciated, the pressure signal may additionally or alternatively be sampled at greater than or less than 500 Hz. Ballistocardiography (BCG) signals, reflecting movement, cardiac activity, and/or respiratory activity, can be obtained through signal processing. For example, this is illustrated in reference to FIG. 42. The smart bed's software and/or hardware as described in the forgoing, can employ machine learning to determine various sleep metrics, including bed presence, bed entry, bed exit, body movements, position changes, time to fall asleep, breathing rate, and heart rate. In some examples, a ballistocardiography signal provides a measure of movement level while the user is lying in bed. For each 10-second window while the user is in bed, the level of movement is compared to a threshold. If the level of activity in a given window is below the threshold, then the given window counts towards restful sleep.

The relationship between distal and proximal skin temperatures is a predictor of sleep onset latency. Increasing distal skin temperature, for instance, can help reduce the time it takes to fall asleep. The sensors placed on the bed in the example smart bed depicted in FIG. 19 can monitor and analyze these temperature gradients, which improve understanding and sleep quality. Further, FIG. 19 highlights the importance of practical, unobtrusive methods for measuring skin temperature during sleep. The example of FIG. 19 includes the sensor strip's placement and configuration, which enables the longitudinal characterization of skin temperature dynamics during sleep.

FIG. 20 is a schematic for generating a sleep program for a bed system. FIG. 20 depicts schematic 2000, a user 2004, a user device 2006, and a bed system 2002 (e.g., bed system 100). In this example, a user 2004 is the user of the bed system 2002. The user 2004 can be the operator of a user device 2006 that is configured to communicate with the bed system 2002. The bed system 2002 can include control circuitry (e.g., control circuitry 334) that can generate control signals to cause heating or cooling elements on the surface of the bed 2002 to change temperature at various times, either in response to user interaction with the bed system 2002, at various pre-programmed times, based on user preference, and/or in response to detecting skin temperature or microclimate temperatures of the user 2004 on the bed system 2002.

Non-limiting examples of user devices 2006 that could be utilized include tablets, smartphones, smart-wearable devices (e.g., finger rings or watches) and other mobile devices equipped with apps or software designed to capture and analyze user data. User devices 2006 can interface with the bed system 2002 to adjust settings based on user input and biometric data collected. For instance, a tablet app can guide a user through a series of questions about their sleep habits and preferences, while a smartphone could use its built-in sensors to track movement and sleep patterns.

The example bed system 2002 further includes a temperature control system 2008. The temperature control system 2008, in some embodiments, includes a computing system (e.g., a computing device 412) that is used in a data processing system associated with the bed system 2002. The temperature control system 2008 can include or be communicatively coupled to the control circuitry that can generate control signals to cause heating or cooling elements on the surface of the bed 2002 to change temperature at various times. The temperature control system 2008 can include one or more processors 2012 (e.g., processor 1102) and memory 2010 (e.g., computer readable memory 1104). The temperature control system 2008 can run applications (e.g., 1110) including, for example, applications to allow the user to interact the bed system 2002. These applications can allow a user to view information about the bed (e.g., sensor readings, sleep metrics), information about themselves (e.g., health conditions detected based on signals sensed at the bed), and/or configure bed system 2002 behavior (e.g., set a sleep program).

The bed system 2002 can further include the pressure sensors (described herein) and temperature sensors (e.g., as described in connection with FIG. 19) to determine the skin temperature of the user 2004. During an example operation of generating a sleep program for a bed system, the memory 2010 of the temperature control system 2008 can store temperature schemes 2014. The temperature schemes 2016-1, 2016-2, 2016-3, 2016-n (collectively referred to herein as “temperature schemes 2016”) can be predetermined and stored to the memory 2010 and executed by the one or more processors 2012. Each of the temperature schemes 2016 can include a temperature setting and a period of time in which to keep the bed system 2002 at the temperature setting.

In some embodiments, there are seven possible temperature settings: OFF, high heat (HH), medium heat (MH) and low heat (LH) heat, and high cooling (HC), medium cooling (MC) and low cooling (LC). In such embodiments, in the heating mode, fresh external air is pulled into the bed system and warmed, and then is distributed into the bed's microclimate. Heating mode warms the microclimate to about 26° C., 33° C., or 38° C. (80° F., 90° F., or 100° F.) depending on the setting. In the cooling mode, warm air from the bed's microclimate is pulled out from the bed and distributed to the external environment. Cooling reduces the microclimate temperature up to about 12° C., 18° C., or 22° C. (53° F., 65° F., or 72° F.) depending on the setting and, in some embodiments, depending on the bedroom temperature. As may be appreciated, there may be more than or fewer than seven temperature settings, which may heat or cool the microclimate to any desired temperature or temperature range. In some embodiments, the bed system 2002 has a heating and cooling mode. In some embodiments, the bed system 2002 includes the cooling mode without the heating mode. In some embodiments, the functionality is integrated into the air mattress and/or foundation system.

In some embodiments, the temperature adjustments are relative to the current temperature of the bed's microclimate (Tm) and the ambient room temperature (Ta). For instance, if Tm exceeds 30° C. (a typical value when a user is in bed), the cooling settings may adjust the temperature as follows: the high cooling (HC) setting reduces Tm by approximately 3° C. every 30 minutes, medium cooling (MC) reduces Tm by about 2° C. every 30 minutes, and low cooling (LC) reduces Tm by around 0.5° C. per 30 minutes. As may be appreciated, the degree of cooling and cooling rate may vary based on design and settings. In another instance, the heating settings adjust the temperature upward, with low heat (LH) increasing Tm by about 1° C. per 30 minutes and medium heat (MH) increasing Tm by about 2° C. per 30 minutes. As may be appreciated, the degree of heating and heating rate may vary based on design and settings. The OFF setting (OF) turns off the temperature heating and cooling of the bed system 2002.

In an example operation, computer memory 2010 storing instructions that, when executed by the at least one processor 2012, cause the at least one processor to operate the temperature control system 2008 to alter a temperature of at least a portion of the bed system 2002 according to a predetermined temperature scheme 2014 comprising four sequential temperature settings 2016, where the predetermined temperature scheme is configured to improve sleep quality of a user of the bed system.

In some embodiments, the temperature of the portion of the bed system is altered from an initial temperature to a first temperature setting of the predetermined temperature scheme, where the first temperature setting is a medium heating setting for a first period of time, alter the temperature of the portion of the bed system from the first temperature setting to a second temperature setting of the predetermined temperature scheme, where the second temperature setting is a low heating setting for a second period of time, refrain from altering the temperature of the portion of the bed system from the second temperature setting because a third temperature setting of the predetermined temperature scheme is also the low heating setting for a third period of time, and alter the temperature of the portion of the bed system from the third temperature setting to a fourth temperature setting of the predetermined temperature scheme, where the fourth temperature setting is the medium heating setting for a fourth period of time.

In another embodiment, the temperature of the portion of the bed system is altered from an initial temperature to a first temperature setting of the predetermined temperature scheme 2016, where the first temperature setting is a low heating setting for a first period of time, alter the temperature of the portion of the bed system from the first temperature setting to a second temperature setting of the predetermined temperature scheme, where the second temperature setting is a medium heating setting for a second period of time, alter the temperature of the portion of the bed system from the second temperature setting to a third temperature setting of the predetermined temperature scheme 2016, where the third temperature setting is an off setting for a third period of time, and refrain from altering the temperature of the portion of the bed system from the third temperature setting because a fourth temperature setting of the predetermined temperature scheme is also the off setting for a fourth period of time.

In another embodiment, the temperature of the portion of the bed system is altered from an initial temperature to a first temperature setting of the predetermined temperature scheme 2016, where the first temperature setting is an off setting for a first period of time, refrain from altering the temperature of the portion of the bed system from the first temperature setting because a second temperature setting of the predetermined temperature scheme is also the off setting for a second period of time, alter the temperature of the portion of the bed system from the second temperature setting to a third temperature setting of the predetermined temperature scheme 2016, where the third temperature setting is a medium cooling setting for a third period of time, and alter the temperature of the portion of the bed system from the third temperature setting to a fourth temperature setting of the predetermined temperature scheme 2016, where the fourth temperature setting is a low cooling setting for a fourth period of time.

In another embodiment, the temperature of the portion of the bed system is altered from an initial temperature to a first temperature setting of the predetermined temperature scheme, where the first temperature setting is a low heating setting for a first period of time, alter the temperature of the portion of the bed system from the first temperature setting to a second temperature setting of the predetermined temperature scheme, where the second temperature setting is a low cooling setting for a second period of time, alter the temperature of the portion of the bed system from the second temperature setting to a third temperature setting of the predetermined temperature scheme, where the third temperature setting is a medium cooling setting for a third period of time, and alter the temperature of the portion of the bed system from the third temperature setting to a fourth temperature setting of the predetermined temperature scheme, where the fourth temperature setting is a low heating setting for a fourth period of time.

As may be appreciated, any suitable combination of temperature settings may be used for the first, second, third, and fourth periods, respectively. As may be further appreciated, fewer than four or more than four periods of time may be used.

FIGS. 21A, 21B, and 21C depict the design of a temperature program to enhance sleep quality. In some embodiments, a temperature scheme will include four temperature settings to enhance the user's sleep experience by improving falling asleep, staying asleep, and waking-up to alertness. For example, to fall asleep, the first and second temperature settings of a temperature scheme 2016 can increase the distal skin temperature of the user thus lowering the user's core body temperature to initiate sleep. For example, this is shown in FIG. 21A. In some embodiments, the distal skin temperature of the user is increased by foot warming. In some embodiments, the microclimate is warmed. In some embodiments where the bed system does not include warming, the bed system can start with a low cooling mode.

To improve staying asleep, the core body temperature of the user should steadily decrease which enables a deeper sleep for the user. For example, this is shown in FIG. 21B. In some embodiments, the microclimate is cooled to facilitate staying asleep. In other embodiments, the temperature feature is OFF if the ambient temperature is greater than about 30° C. Any suitable ambient temperature may be used to trigger between one or more temperature settings.

To improve waking-up, the core body temperature of the user should increase thereby increasing alertness and enhancing the wake-up experience of the user. For example, this is shown in FIG. 21C. In some embodiments, the microclimate is slightly cooled down. In other embodiments, the microclimate is slightly warmed.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES

Example Study Implementation: Example 1

Overview: Temperature regulation plays an essential role in sleep, as changes in core body temperature and skin temperature are closely linked to the sleep-wake cycle. Specifically, a decrease in core body temperature, facilitated by an increase in distal skin temperature, is associated with the process of sleep initiation. Recognizing the importance of temperature in sleep, disclosed herein are advanced platforms capable of modulating the sleep microclimate—the environment between the bed covers, the body, and the mattress. These platforms manage temperature by controlling the flow of warm or cool air through a thermal module equipped with a fan, allowing for adjustable cooling and heating intensities. The study disclosed herein identifies specific temperature programs that are associated with improved sleep quality, offering a tailored approach to temperature management during sleep.

Materials and Methods: The study was conducted in two parts: an in-lab component and an at-home component, each designed to assess the impact of various temperature programs on sleep quality. Thirty-three volunteers were recruited (20M/13F), with a mean age of 48.4 (standard deviation SD: 10.9) years, mean body mass index (BMI) 29.8 (SD: 7.1) Kg/m2 (see Table 1). Sleep data were collected in-lab and in-home.

TABLE 1
Demographic data for study participants.

					Type of
		Height	Weight	BMI	recorded
Gender	Age	(m)	(Kg)	(Kg/m2)	data

Male	41	1.75	112.5	36.6	In-lab &
					in-home
Female	37	1.55	68.6	28.6	In-lab &
					in-home
Male	45	1.83	117.5	35.1	In-lab &
					in-home
Female	39	1.72	68.1	23.0	In-lab &
					in-home
Male	42	1.95	92.7	24.4	In-lab &
					in-home
Female	42	1.61	61.8	23.8	In-lab &
					in-home
Male	43	1.76	87.6	28.3	In-lab &
					in-home
Male	31	1.86	99.4	28.8	In-lab &
					in-home
Female	49	1.57	81.6	33.1	In-lab &
					in-home
Female	49	1.60	78.8	30.9	In-lab &
					in-home
Male	51	1.76	85.3	27.5	In-lab &
					in-home
Female	45	1.60	78.8	30.9	In-lab &
					in-home
Female	57	1.55	81.6	34.0	In-lab &
					in-home
Male	41	1.75	81.6	26.7	In-lab &
					in-home
Male	57	1.79	102.5	32.0	In-lab &
					in-home
Female	42	1.59	84.7	33.5	In-lab &
					in-home
Male	50	1.76	91.5	29.5	In-lab
Female	40	1.73	88.3	29.5	In-lab
Female	64	1.78	81.6	25.8	In-home
Male	67	1.63	71.2	26.9	In-home
Male	65	1.75	86.6	28.2	In-home
Male	57	1.78	102.1	32.3	In-home
Female	45	1.70	120.2	41.5	In-home
Male	46	1.70	138.3	47.8	In-home
Male	59	1.83	99.8	29.8	In-home
Male	46	1.80	79.4	24.4	In-home
Male	38	1.80	50.8	15.6	In-home
Male	72	1.83	89.4	26.7	In-home
Male	36	1.83	86.2	25.8	In-home
Male	57	1.75	78.9	25.7	In-home
Female	71	1.60	49.9	19.5	In-home
Female	39	1.63	136.1	51.5	In-home
Male	34	1.80	83.9	25.8	In-home

TABLE 2
List of programs for in-lab portion.

		Setting 1	Setting 2	Setting 3
	Program	(Hours 0	(Hours 3	(Hours 6
	ID	to 3)	to 6)	to 9)
	In-Lab 1	OFF	MH	HC
	In-Lab 2	LC	MC	LH
	In-Lab 3	MH	LC	OFF
	In-Lab 4	LH	HC	MC
	In-Lab 5	LC	LH	MH
	In-Lab 6	MC	LC	HC
	In-Lab 7	LH	MC	MC
	In-Lab 8	HC	MH	LC
	In-Lab 9	MC	HC	LH
	In-Lab 10	MH	LC	MH
	In-Lab 11	MC	OFF	MH
	In-Lab 12	HC	LC	LH
	In-Lab 13	HC	OFF	MC
	In-Lab 14	MH	LH	LC
	In-Lab 15	OFF	HC	MC
	In-Lab 16	LH	MH	LC
	In-Lab 17	HC	LH	OFF
	In-Lab 18	LC	MC	MH
	In-Lab 19	LH	LC	HC
	In-Lab 20	MH	OFF	MC
	In-Lab 21	LC	MH	LH
	In-Lab 22	MC	HC	OFF
	In-Lab 23	MH	MC	LC
	In-Lab 24	OFF	LH	HC
	In-Lab 25	MH	LC	OFF
	In-Lab 26	MC	HC	LH
	In-Lab 27	LC	MC	MH
	In-Lab 28	LH	OFF	HC
	In-Lab 29	LC	MC	MH
	In-Lab 30	LH	OFF	HC
	In-Lab 31	MC	LH	LC
	In-Lab 32	HC	MH	OFF
	In-Lab 33	LH	HC	MC
	In-Lab 34	OFF	LC	MH
	In-Lab 35	HC	OFF	LH
	In-Lab 36	MH	MC	LC

Polysomnography (PSG) signals were recorded in-lab and these included electroencephalogram (EEG) signals from standard locations F3, F4, C3, C4, O1, O2, electromyogram (EMG), electro-oculogram (EOG), electrocardiogram (ECG), breathing signals, and limb movement electrodes. All these signals were acquired at a sampling frequency of 200 Hz.

Procedure: Each participant spent two nights in the sleep lab, where polysomnography (PSG) signals were recorded to monitor sleep stages, brain activity, muscle activity, and other physiological parameters. During these sessions, participants were exposed to 36 different temperature programs, designed to evaluate the effects of varying cooling and heating sequences on sleep quality.

Distal skin temperature was measured using an iButton (type DS1922L; Maxim, Dallas Semiconductor Corp., Dallas, TX, USA) positioned on the right foot's arch. Proximal skin temperature was measured using an iButton positioned just below the left collar bone on the participant's chest. The iButton device offers two resolutions, 0.5° C. or 0.0625° C. (iButton Link); we used the latter in this study. Smart bed data including the sleep session and the TSS data were also collected from the in-lab sleep sessions.

Data Collection: Polysomnography: PSG data were collected throughout the night to track sleep stages and other physiological responses.

Temperature Data: Temperature data were collected from a sensor array integrated into the bed system. The sensor array monitored the temperature within the sleep microclimate, including the space between the bed covers, the body, and the mattress.

External Sensors: Additional sensors were used to capture supplementary data relevant to the study's objectives.

In-home portion description: The in-home portion of the study included the collection of data from the TSS, proximal skin temperature and distal temperature using iButtons positioned near the left collar bone and on the right foot's arch as in the lab condition. Participants wore the Empatica E4 wristband on the non-dominant hand during sleep to collect distal wrist skin temperature, photoplethysmography signals, and electrodermal activity.

TABLE 3
List of programs for in-home portion.

	Setting 1	Setting 2	Setting 3	Setting 4
Program	(Hours 0	(Hours 2	(Hours 4	(Hours 6
ID	to 2)	to 4)	to 6)	to 8)
In-home 1	OFF	LC	MC	HC
In-home 2	LC	MC	OFF	OFF
In-home 3	LH	LC	MC	LH
In-home 4	OFF	OFF	OFF	OFF
In-home 5	LH	MH	OFF	OFF
In-home 6	LC	LC	LH	LH
In-home 7	OFF	OFF	OFF	OFF
In-home 8	MH	LH	LH	MH
In-home 9	OFF	OFF	MH	LH
In-home 10	OFF	OFF	OFF	OFF
In-home 11	OFF	OFF	HC	LC
In-home 12	LC	MC	HC	LC
In-home 13	OFF	OFF	OFF	OFF
In-home 14	LH	MH	MH	LH

Procedure: Participants were instructed to use the bed system at home for 14 nights. During this period, they were exposed to a subset of temperature programs identified in the in-lab study. The at-home setting allowed for the assessment of temperature program efficacy under typical sleep conditions.

For both, in-lab and in-home the data from an iButton affixed on the surface of the bed was also collected to gain insight into changes on the temperature surface due to the application of temperature programs. Study participants were asked about their usual bedtime and the start of temperature programs (program onset) was set to start at that time.

FIG. 22 depicts an overview of the data analysis methods used in the study. In-home data analysis methods: The sleep session data collected from participants during their study involvement was extracted from the secure computing system (e.g., a secure cloud). Two types of analyses were performed, “intention-to-treat” (ITT) based on the temperature program assigned to a given sleep session and “as-treated” (AT) based on the participant's actual bedtime. The latter considers that in the home environment, study participants are less likely to strictly follow bedtime recommendations. For the AT analysis, sleep session data were included if the bedtime (as detected by the smart bed) was in the time interval starting from TB (=1.0) hours before temperature program onset and ending TA (=1.5) hours after onset of the temperature program (see FIG. 23). The temperature settings applied to sleep sessions were numerically encoded as described in Table 4 which assumes a linear increase in the temperature of the microenvironment depending on the applied temperature setting. An in-home program is defined by a sequence of four integer numbers S1, S2, S3, and S4∈{−3, . . . , +2} that are respectively applied to first, second, third and fourth 2-hour sleep segments.

TABLE 4
Temperature Setting Encoding

	Temperature	Numerical
	Setting	Encoding

	High Cooling	−3
	Medium Cooling	−2
	Low Cooling	−1
	Off	0
	Low Heating	+1
	Medium Heating	+2

Linear mixed models were used to quantify the effect of temperature settings on each considered metric (i.e., sleep duration, restful sleep duration, sleep quality score SIQ, heart rate, heart rate variability, or respiratory rate). For a given sleep metric, the corresponding linear mixed model considered the metric as dependent variable, S1 to S4 as the fixed effect factors, and the participant ID as random factor. The significant model coefficients, which are associated with specific Sx, were identified and analyzed separately to understand the effect of the Sx setting with respect to the default setting Sx=0 (which corresponds to OFF).

The iButton data collected from the in-bed sensor along with the temperature sensor strip data were used to verify delivery of temperature settings. The data from the iButtons that measure proximal and foot-distal skin temperature, were aggregated at the session and sleep-segment levels to analyze the effect on them of the temperature settings. The objective of this analysis is to determine whether the effect of the temperature settings on sleep metrics is mediated by the values of proximal and distal-foot temperatures.

The data from the Empatica wristband enables the quantification of distal wrist temperature, and because of its integrated photoplethysmography sensor higher temporal resolution versions of heart rate and heart rate variability. However, the reliability of the Empatica device was suboptimal as described in the Results section which prevented us from fully utilizing the data provided by this device. Such low reliability may be due to the imminent decommissioning of the Empatica Embrace E4 in favor of the latest wristband.

In-Lab Data analysis: The PSG data for each sleep session was first annotated by an experienced sleep scientist for manual sleep staging on the basis of 30-second-long epochs. Using the sequence of sleep stages (hypnogram), the following sleep architecture metrics were calculated: (i) total sleep time (TST), where the TST is the amount of time actually spent sleeping in a planned sleep episode. Sum of all REM and NREM sleep provides the total sleep time (TST). The awake time after sleep onset (WASO) or before onset (WBSO) is not included in Total sleep time; (ii) sleep onset latency (SOL), where SOL is the time elapsed from sleep intent to sleep onset (for in-lab studies, this is the time between lights-off (i.e., when the sleep technician turns the lights off) and sleep onset); (iii) wake after sleep onset (WASO), where WASO is the total time of wakefulness after sleep onset caused by disruptions; and (iv) Duration of each sleep stage. The sleep states were aggregated into four: deep (N3), light (N2 and N1), rem, and wake.

The electrocardiogram (ECG) signals were processed using a Wavelet based algorithm to detect cardiac R-peaks which were used to estimate mean heart rate, and (time and frequency domain) heart rate variability for each 30-second epoch. The EEG signals were processed in the spectral domain to compute the mean power values per 30-second epoch in the Delta (0.5 to 4 Hz), Theta (4 to 8 Hz), Alpha (8 to 12 Hz), Spindle (11 to 16 Hz), and Beta (15 to 30 Hz) bands. The log-ratio between Beta and Delta power was also calculated as this is an indicator of sleep deepening during the falling asleep process. A high (low) beta/delta ratio indicates lower (higher) quality of the falling asleep process.

The iButton data from all locations was also aggregated in 30-second epochs and the timing was adjusted to match the hypnogram timing. In a similar manner as for the in-home data, linear mixed models were used to quantify the effect of temperature settings on sleep onset latency, sleep duration, the duration of each sleep stage, wake after sleep duration, and EEG-derived spectral power.

Because of the relatively lower number of sleep sessions collected in-lab (36 sessions) compared to the number of in-home sessions (>300), the encoding of temperature settings for the in-lab analysis ignores intensity of the temperature setting and uses three values (+1 for any type of heating, −1 for any type of cooling, and 0 for Off).

To determine the statistical significance of the difference in sleep metrics associated with temperature settings, for instance sleep onset latency between starting the temperature program with warming, cooling, or OFF, an analysis of variance (ANOVA) was performed after fitting a linear model.

In-home data analysis: The number of sessions per participant in the in-home portion of the study, for the ITT and AT analyses is listed in Table 5. The session selection for the AT analysis was done using the criteria in FIG. 4 (for TB=1 hour and TA=1.5 hour; AT1) and for (TB=−∞ TA=1 hour; AT2).

TABLE 5
Number of in-home sleep sessions for ITT and AT analyses.

	Number of sessions AT1	Number of sessions AT2
Number of	(TB = 1 hour and	(TB = −∞ hour
sessions ITT	TA = 1.5 hour)	and TA = 1 hour)

8	5	4
3	2	3
8	2	5
10	3	9
12	5	10
14	8	3
14	8	4
14	8	9
11	7	4
10	4	9
8	2	8
10	5	9
10	4	10
11	9	10
11	4	6
11	6	8
13	9	12
14	14	14
13	2	12
15	15	14
16	16	16
16	16	15
12	12	11
14	7	14
14	14	14
14	14	13
13	13	13
14	12	13
12	12	12
11	9	7
13	12	13
Total: 369	Total: 259	Total: 304

Sleep quality (SIQ score): The linear mixed model results for SIQ score depending on encoded temperature settings for the 1st to 4th segment are reported in Table 6 for the ITT analysis, in Table 7 for the AT1 analysis, and in Table 8 for the AT2 analysis. The statistically significant (P<0.05) associations throughout these examples are emphasized with a t in the respective row. Trending associations (0.05≤P<0.1) are emphasized with a ‡ in the respective row.

The gender-agnostic ITT analysis suggests that a higher SIQ score results from starting the first section with heating, followed by cooling in the second section, and ending with cooling in the last section. The latter increases the SIQ score but to a lesser extent. Indeed, the absolute value of the coefficient associated with the fourth segment is 1.81 versus 3.75 and 3.09 for the first and second segments respectively. For men, cooling in the second and fourth segments increases the SIQ score but warming in the first segment is not significantly associated with the SIQ score. For women, heating in the first segment and cooling in the second segment are significantly associated with higher SIQ score.

The gender-agnostic AT1 analysis (see Table 7) did not show any significance; although there is a trending result (P=0.07) indicating that cooling in the second segment increases SIQ score. Such a trending association is also present for men. For women, no significant associations were found.

The gender agnostic AT2 analysis shows significant increase of the SIQ score by heating in the first segment followed by cooling in the second segment (see Table 8). This is not exactly identical to the ITT results; although cooling in the fourth segment has a trending (P=0.06) effect on increasing SIQ score. For men, cooling in the second segment has a positive influence on the SIQ score. For women, heating in the first segment leads to higher SIQ score. These results are consistent with the ITT analysis.

TABLE 6
ITT analysis - Linear mixed model.
SIQ score ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	74.095	1.24	0
	† 1st temp. setting	3.751	1.421	0.008
	† 2nd temp. setting	−3.087	1.045	0.003
	3rd temp. setting	1.183	0.715	0.098
	† 4th temp. setting	−1.808	0.871	0.038

Men

	Intercept	75.196	1.405	0
	1st temp. setting	2.751	1.699	0.105
	† 2nd temp. setting	−2.655	1.243	0.033
	3rd temp. setting	1.411	0.841	0.093
	† 4th temp. setting	−2.10	1.04	0.043

Women

	Intercept	72.174	2.367	0
	† 1st temp. setting	5.505	2.564	0.032
	† 2nd temp. setting	−4.0	1.904	0.036
	3rd temp. setting	0.815	1.339	0.543
	4th temp. setting	−1.288	1.581	0.415

TABLE 7
AT1 analysis - Linear mixed model.
SIQ score ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	76.4	1.41	0
	1st temp. setting	0.99	1.44	0.49
	‡ 2nd temp. setting	−1.84	1.03	0.07
	3rd temp. setting	0.92	0.73	0.21
	4th temp. setting	−1.05	0.83	0.21

Men

	Intercept	78.5	1.36	0
	1st temp. setting	−0.26	1.43	0.86
	‡ 2nd temp. setting	−1.93	1.03	0.06
	3rd temp. setting	1.09	0.73	0.14
	4th temp. setting	−0.86	0.86	0.32

Women

	Intercept	73.1	2.88	0
	1st temp. setting	3.96	3.34	0.24
	2nd temp. setting	−2.11	2.33	0.37
	3rd temp. setting	0.82	1.65	0.62
	4th temp. setting	−1.43	1.79	0.43

TABLE 8
AT2 analysis - Linear mixed model.
SIQ score ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	75.65	1.342	0
	† 1st temp. setting	2.798	1.363	0.04
	† 2nd temp. setting	−2.719	0.989	0.006
	3rd temp. setting	0.828	0.69	0.23
	‡ 4th temp. setting	−1.489	0.805	0.064

Men

	Intercept	76.934	1.635	0
	1st temp. setting	1.223	1.492	0.412
	† 2nd temp. setting	−2.344	1.088	0.031
	3rd temp. setting	1.089	0.735	0.139
	4th temp. setting	−1.451	0.874	0.097

Women

	Intercept	73.359	2.425	0
	† 1st temp. setting	5.708	2.8	0.041
	‡ 2nd temp. setting	−3.508	2.005	0.08
	3rd temp. setting	0.295	1.5	0.844
	4th temp. setting	−1.699	1.677	0.311

Sleep duration: The linear mixed model results for sleep duration depending on encoded temperature settings for the 1st to 4th segment is reported in Table 9 for the ITT analysis, in Table 10 for the AT1 analysis, and in Table 11 for the AT2 analysis. Neither the ITT nor the AT1 analyses show any significant association between sleep duration and the temperature settings. However, the AT2 analysis shows significant association between temperature settings and sleep duration for men but not for women. Cooling in the first segment, followed by heating in the second segment, and heating in the 4th segment are associated with longer sleep duration.

TABLE 9
ITT analysis - Linear mixed model. Sleep
duration ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	7.625	0.174	0
	1st temp. setting	−0.032	0.154	0.836
	2nd temp. setting	0.072	0.113	0.523
	3rd temp. setting	−0.054	0.078	0.484
	4th temp. setting	0.093	0.094	0.327

Men

	Intercept	7.659	0.219	0
	1st temp. setting	−0.145	0.181	0.423
	2nd temp. setting	0.094	0.132	0.477
	3rd temp. setting	−0.047	0.09	0.602
	4th temp. setting	0.101	0.111	0.364

Women

	Intercept	7.562	0.298	0
	1st temp. setting	0.163	0.286	0.568
	2nd temp. setting	0.029	0.212	0.89
	3rd temp. setting	−0.061	0.149	0.682
	4th temp. setting	0.077	0.176	0.66

TABLE 10
AT1 analysis - Linear mixed model. Sleep
duration ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	7.36	0.135	0
	1st temp. setting	0.037	0.132	0.778
	2nd temp. setting	−0.003	0.095	0.979
	3rd temp. setting	0.011	0.067	0.872
	4th temp. setting	−0.023	0.077	0.766

Men

	Intercept	7.52	0.147	0
	1st temp. setting	−0.054	0.144	0.710
	2nd temp. setting	0.023	0.104	0.829
	3rd temp. setting	0.030	0.074	0.688
	4th temp. setting	−0.009	0.087	0.920

TABLE 11
AT2 analysis - Linear mixed model. Sleep
duration ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	7.9	0.184	0
	1st temp. setting	−0.257	0.154	0.094
	† 2nd temp. setting	0.256	0.112	0.022
	† 3rd temp. setting	−0.171	0.078	0.028
	† 4th temp. setting	0.197	0.091	0.03

Men

	Intercept	7.953	0.227	0
	† 1st temp. setting	−0.362	0.168	0.031
	† 2nd temp. setting	0.25	0.122	0.041
	3rd temp. setting	−0.158	0.083	0.056
	† 4th temp. setting	0.203	0.098	0.039

Women

	Intercept	7.812	0.326	0
	1st temp. setting	−0.053	0.32	0.868
	2nd temp. setting	0.259	0.23	0.259
	3rd temp. setting	−0.18	0.172	0.297
	4th temp. setting	0.169	0.192	0.378

Restful Sleep Percentage: The linear mixed model results for restful sleep percentage depending on encoded temperature settings for the 1st to 4th segment is reported in Table 12 for the ITT analysis, in Table 13 for the AT1 analysis, and in Table 14 for the AT2 analysis. Neither the ITT nor the AT1 analyses show any significant effect of the temperature settings on restful sleep.

However, the gender agnostic AT2 analysis (Table 14) suggests that higher percent restful sleep can be accomplished by cooling in the second segment. This effect appears to be more pronounced in women (P=0.098) than in men (0.189).

TABLE 12
ITT analysis - Linear mixed model. Restful
sleep percent ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	88.231	0.767	0
	1st temp. setting	0.225	0.459	0.624
	2nd temp. setting	−0.526	0.338	0.119
	3rd temp. setting	0.157	0.231	0.498
	4th temp. setting	0.007	0.281	0.981

Men

	Intercept	88.34	0.9	0
	1st temp. setting	0.134	0.545	0.806
	2nd temp. setting	−0.48	0.398	0.228
	3rd temp. setting	0.287	0.269	0.287
	4th temp. setting	−0.13	0.333	0.696

Women

	Intercept	88.026	1.445	0
	1st temp. setting	0.368	0.84	0.661
	2nd temp. setting	−0.635	0.625	0.31
	3rd temp. setting	−0.106	0.439	0.81
	4th temp. setting	0.274	0.517	0.596

TABLE 13
AT1 analysis - Linear mixed model. Restful
sleep percent ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	88.97	0.779	0
	1st temp. setting	−1.31	1.017	0.196
	2nd temp. setting	0.654	1.003	0.515
	3rd temp. setting	−0.241	1.155	0.835
	4th temp. setting	0.503	0.972	0.605

Men

	Intercept	88.97	0.895	0
	1st temp. setting	−0.653	0.600	0.276
	2nd temp. setting	−0.126	0.434	0.772
	3rd temp. setting	0.312	0.308	0.312
	4th temp. setting	0.023	0.362	0.650

Women

	Intercept	89.01	1.427	0
	1st temp. setting	−0.718	1.105	0.516
	2nd temp. setting	0.259	0.777	0.739
	3rd temp. setting	−0.078	0.550	0.888
	4th temp. setting	0.325	0.595	0.586

TABLE 14
AT2 analysis - Linear mixed model. Restful
sleep percent ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	88.328	0.802	0
	1st temp. setting	0.292	0.501	0.56
	† 2nd temp. setting	−0.765	0.364	0.036
	3rd temp. setting	0.276	0.254	0.277
	4th temp. setting	−0.028	0.296	0.924

Men

	Intercept	88.535	0.93	0
	1st temp. setting	−0.136	0.573	0.812
	2nd temp. setting	−0.549	0.419	0.189
	3rd temp. setting	0.333	0.282	0.238
	4th temp. setting	0.007	0.336	0.984

Women

	Intercept	87.901	1.527	0
	1st temp. setting	1.065	0.985	0.28
	‡ 2nd temp. setting	−1.171	0.707	0.098
	3rd temp. setting	0.119	0.529	0.822
	4th temp. setting	−0.088	0.592	0.881

Heartrate: The linear mixed model results for heartrate depending on encoded temperature settings for the 1st to 4th segment is reported in Table 15 for the ITT analysis, in Table 16 for the AT1 analysis, and in Table 17 for the AT2 analysis.

The ITT and AT1 analyses (see Table 15 and Table 16) show a significant heartrate reducing effect of cooling in the first segment for men. These results, together with those of Table 6 (higher SIQ score in men for cooling in the 2nd and 4th segments) suggest that men may benefit more from cooling compared to off. No other significant associations were found in the AT2 analysis.

TABLE 15
ITT analysis - Linear mixed model.
Heartrate ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	61.754	0.995	0
	1st temp. setting	0.644	0.439	0.143
	2nd temp. setting	−0.31	0.323	0.337
	3rd temp. setting	0.24	0.221	0.277
	4th temp. setting	−0.05	0.269	0.854

Men

	Intercept	60.747	1.332	0
	† 1st temp. setting	1.079	0.526	0.04
	2nd temp. setting	−0.559	0.385	0.146
	3rd temp. setting	0.186	0.26	0.474
	4th temp. setting	−0.065	0.322	0.839

Women

	Intercept	63.471	1.399	0
	1st temp. setting	−0.104	0.788	0.895
	2nd temp. setting	0.175	0.586	0.765
	3rd temp. setting	0.376	0.412	0.36
	4th temp. setting	−0.072	0.486	0.882

TABLE 16
ATI analysis - Linear mixed model.
Heartrate ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	61.597	0.995	0
	1st temp. setting	0.821	0.537	0.126
	2nd temp. setting	−0.383	0.386	0.320
	3rd temp. setting	0.174	0.274	0.524
	4th temp. setting	−0.106	0.312	0.733

Men

	Intercept	60.39	1.260	0
	† 1st temp. setting	1.275	0.605	0.035
	2nd temp. setting	−0.627	0.438	0.152
	3rd temp. setting	0.176	0.310	0.570
	4th temp. setting	−0.296	0.365	0.418

Women

	Intercept	63.501	1.523	0
	1st temp. setting	−0.058	1.105	0.958
	2nd temp. setting	0.181	0.778	0.816
	3rd temp. setting	0.271	0.554	0.625
	4th temp. setting	0.076	0.600	0.900

TABLE 17
AT2 analysis - Linear mixed model.
Heartrate ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	61.764	1.016	0
	1st temp. setting	0.263	0.476	0.58
	2nd temp. setting	−0.114	0.346	0.742
	3rd temp. setting	0.22	0.241	0.362
	4th temp. setting	−0.108	0.281	0.701

Men

	Intercept	60.722	1.339	0
	1st temp. setting	0.764	0.561	0.174
	2nd temp. setting	−0.405	0.41	0.323
	3rd temp. setting	0.204	0.276	0.461
	4th temp. setting	−0.15	0.329	0.649

Women

	Intercept	63.569	1.499	0
	1st temp. setting	−0.663	0.892	0.457
	2nd temp. setting	0.447	0.64	0.484
	3rd temp. setting	0.292	0.479	0.542
	4th temp. setting	−0.037	0.537	0.945

Heartrate variability: The linear mixed model results for heartrate variability depending on encoded temperature settings for the 1st to 4th segment is reported in Table 18 for the ITT analysis, in Table 19 for the AT1 analysis, and in Table 20 for the AT2 analysis. No statistically significant associations were found in the ITT and AT1 analyses.

However, the AT2 analysis (Table 20) suggests that warming in the third segment increases HRV for women.

TABLE 18
ITT analysis - Linear mixed model. Heart
rate variability ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	107.263	8.379	0
	1st temp. setting	−3.013	2.703	0.265
	2nd temp. setting	2.067	1.984	0.297
	3rd temp. setting	−0.416	1.358	0.76
	4th temp. setting	0.608	1.668	0.715

Men

	Intercept	108.322	12.835	0
	1st temp. setting	−3.857	3.137	0.219
	2nd temp. setting	3.469	2.284	0.129
	3rd temp. setting	−2.116	1.546	0.171
	4th temp. setting	1.821	1.943	0.348

Women

	Intercept	104.62	7.889	0
	1st temp. setting	−1.184	5.048	0.815
	2nd temp. setting	−0.566	3.755	0.88
	3rd temp. setting	2.741	2.635	0.298
	4th temp. setting	−1.469	3.11	0.637

TABLE 19
AT1 analysis - Linear mixed model. Heart
rate variability ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	106.181	8.533	0
	1st temp. setting	1.846	2.589	0.518
	2nd temp. setting	−0.240	2.063	0.907
	3rd temp. setting	0.632	1.456	0.665
	4th temp. setting	−1.329	1.672	0.426

Men

	Intercept	99.146	11.039	0
	1st temp. setting	0.211	3.192	0.947
	2nd temp. setting	0.904	2.320	0.697
	3rd temp. setting	−0.625	1.634	0.702
	4th temp. setting	−0.851	1.940	0.661

Women

	Intercept	115.655	13.653	0
	1st temp. setting	5.685	5.0962	0.340
	2nd temp. setting	−2.332	4.213	0.580
	3rd temp. setting	3.295	2.989	0.270
	4th temp. setting	−2.399	3.225	0.457

TABLE 20
AT2 analysis - Linear mixed model. Heart
rate variability ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	107.918	8.528	0
	1st temp. setting	−1.042	2.494	0.676
	2nd temp. setting	0.642	1.807	0.723
	3rd temp. setting	1.007	1.259	0.424
	4th temp. setting	−0.774	1.488	0.603

Men

	Intercept	109.604	13.038	0
	1st temp. setting	−4.122	2.886	0.153
	2nd temp. setting	2.426	2.099	0.248
	3rd temp. setting	−0.912	1.417	0.52
	4th temp. setting	1.194	1.716	0.486

Women

	Intercept	103.873	8.359	0
	1st temp. setting	5.607	4.726	0.235
	2nd temp. setting	−3.298	3.396	0.331
	3rd temp. setting	5.475	2.537	0.031
	4th temp. setting	−4.994	2.842	0.79

Breathing rate: The linear mixed model results for breathing rate depending on encoded temperature settings for the 1st to 4th segment is reported in Table 21 for the ITT analysis, in Table 22 for the AT1 analysis, and in Table 23 for the AT2 analysis. No statistically significant associations were found in none of these analyses. A trending result (P=0.07) was found in the ITT gender agnostic (Table 21) result suggesting the cooling in the second segment decreases breathing rate. A similar trend (P=0.08) was found in the AT2 analysis (Table 23). This is consistent with the SIQ results in Section 4.1.1.

TABLE 21
ITT analysis - Linear mixed model. Breathing
rate ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	15.252	0.287	0
	1st temp. setting	−0.084	0.091	0.355
	‡ 2nd temp. setting	0.122	0.067	0.069
	3rd temp. setting	−0.003	0.046	0.953
	4th temp. setting	−0.045	0.056	0.424

Men

	Intercept	15.384	0.409	0
	1st temp. setting	−0.052	0.114	0.648
	2nd temp. setting	0.106	0.083	0.201
	3rd temp. setting	0.008	0.056	0.887
	4th temp. setting	−0.046	0.069	0.504

Women

	Intercept	15.049	0.375	0
	1st temp. setting	−0.14	0.155	0.364
	2nd temp. setting	0.149	0.115	0.195
	3rd temp. setting	−0.025	0.081	0.755
	4th temp. setting	−0.039	0.095	0.684

TABLE 22
AT1 analysis - Linear mixed model. Breathing
rate ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	15.238	0.299	0
	1st temp. setting	−0.013	0.097	0.891
	2nd temp. setting	0.056	0.070	0.424
	3rd temp. setting	−0.008	0.049	0.875
	4th temp. setting	−0.052	0.056	0.360

Men

	Intercept	15.429	0.445	0
	1st temp. setting	−0.023	0.109	0.832
	2nd temp. setting	0.064	0.079	0.414
	3rd temp. setting	−0.033	0.056	0.557
	4th temp. setting	−0.060	0.066	0.363

Women

	Intercept	14.992	0.363	0
	1st temp. setting	0.031	0.200	0.897
	2nd temp. setting	0.040	0.142	0.775
	3rd temp. setting	0.054	0.100	0.586
	4th temp. setting	−0.053	0.900	0.627

TABLE 23
AT2 analysis - Linear mixed model. Breathing
rate ~ temperature setting.

	Coefficients	Std Err.	P

All participants

	Intercept	15.251	0.29	0
	1st temp. setting	−0.129	0.095	0.174
	‡ 2nd temp. setting	0.123	0.069	0.075
	3rd temp. setting	−0.059	0.048	0.22
	4th temp. setting	−0.026	0.056	0.642

Men

	Intercept	15.393	0.407	0
	1st temp. setting	−0.131	0.111	0.238
	‡ 2nd temp. setting	0.138	0.081	0.089
	3rd temp. setting	−0.061	0.054	0.263
	4th temp. setting	−0.01	0.065	0.879

Women

	Intercept	15.018	0.392	0
	1st temp. setting	−0.121	0.183	0.507
	2nd temp. setting	0.095	0.131	0.471
	3rd temp. setting	−0.057	0.098	0.56
	4th temp. setting	−0.057	0.11	0.605

Falling asleep process: The analysis of the effect of temperature programs on falling asleep was analyzed based on the initial temperature setting, i.e. the program that was applied in the first three hours of the in-lab sessions. This section considers sleep onset latency (SOL), and changes from lights-off in sleep depth, heartrate and foot temperature. The changes in sleep depth, heartrate and foot temperature were estimated by subtracting the corresponding mean values in the period from 2 minutes before to 2 minutes after lights off. The boxplot of sleep onset latency in minutes versus the first temperature setting is shown in FIG. 24A (which also considers intensity) and in FIG. 24B (which only considers heating or cooling regardless of the intensity). The analysis of variance between SOL and the six temperature settings resulted in a non-significant F value of 0.53. The aggregated boxplot (FIG. 24B) suggests that starting with warming may be associated with shorter sleep latency compared to starting with cooling or off. However, the analysis of variance results in an F value of 1.152 which has a non-significant p value (0.33) associated with it.

Sleep depth change during falling asleep: Depending on the initial temperature setting, the change in sleep depth (i.e. the logarithm of the EEG beta power over EEG delta power) versus time was analyzed taking as reference the time from lights-off (i.e. sleep intent; see FIGS. 25A-25D) and from sleep onset (see FIG. 26). Because they start at sleep onset, the curves in FIG. 26A have more monotonous decrease compared to FIG. 25A.

The tick lines in FIG. 25A and FIG. 26A show the mean normalized sleep depth curves for each temperature setting. The dashed lines correspond to the standard errors.

The orange bars in FIG. 25B and FIG. 26B indicate the times where sleep depth is significantly different (P=0.05 was taken as significance threshold) in the heating setting with respect to the cooling setting. Starting from 15 minutes after lights-off (FIG. 25B), sleep is significantly deeper in the heating condition compared to cooling. Starting from 30 minutes after sleep onset (FIG. 26B) sleep depth is significantly different in the heating condition compared to cooling.

The orange bars in FIG. 25C and FIG. 26C indicate the times where sleep depth is significantly different in the heating condition compared with Off (P=0.05 was taken as significance threshold). Sleep is significantly deeper in the heating condition compared to Off during a brief period starting from 45 minutes after lights-off (FIG. 25C). However, a (non-statistically significant) trend can be observed showing that sleep remains deeper even 60 minutes after lights-off. Taking sleep onset as reference (FIG. 26C), sleep is significantly deeper in the heating condition compared to Off in the first ten minutes and the from 30 minutes onwards.

The blue bars in FIG. 25D and FIG. 26D indicate the times where sleep depth is significantly different in the cooling condition compared with Off (P=0.05 was taken as significance threshold). Sleep is significantly deeper in the cooling condition compared to Off during a few minutes between 20 and 30 minutes after lights-off (FIG. 25D). Taking sleep onset as reference (FIG. 26D), sleep depth is not significantly different between cooling and Off.

Starting with heating promotes faster sleep deepening compared to cooling or off. This result is consistent with the sleep onset latency boxplot (FIG. 24) which suggests (without reaching statistical significance) that a faster sleep onset is associated with heating first.

FIGS. 27A-27D depict a change in heartrate during the falling asleep process depending on the first temperature setting taking as reference “lights off”. (A) Mean normalized heartrate curves for each temperature setting (dashed lines indicate the standard errors). (B) P-value comparing cooling versus heating. Significant values are indicated in orange. (C) P-value comparing Off versus heating. Significant values are identified by blue bars. (D) P-value comparing Off versus Cooling. Significant values are indicated by blue bars.

FIGS. 28A-28D depict a change in heartrate during falling asleep depending on the first temperature setting taking as reference “sleep onset time”. (A) Mean normalized heartrate curves for each temperature setting (dashed lines indicate the standard errors). (B) P-value comparing cooling versus heating. Significant values are indicated in orange. (C) P-value comparing Off versus heating. Significant values are indicated in orange. (D) P-value comparing Off versus cooling. Significant values would be identified by blue bars.

Foot temperature change: The change in distal foot temperature associated with the first temperature setting was analyzed taking as reference the time of lights-off. FIGS. 29A-29D depict a change in foot temperature during the falling asleep process depending on the first temperature setting. Lights off was taken as reference. (B) Statistical comparison heating versus cooling (significant values are shown as orange bars). (C) Statistical comparison heating versus Off (significant values are shown as orange bars). (D) Statistical comparison cooling versus off (significant values are shown as blue bars). FIG. 29A shows the mean foot temperature change (using time of lights-off as reference) for the cooling, off, and heating settings. The analysis of statistical significance (P=0.05 was taken as significance threshold) shows that foot temperature is significantly higher for the heating compared to cooling condition (see FIG. 29B) starting at 60 minutes after lights-off. The temperature of the feet is not significantly different at any time between Off and heating conditions. However, a trend can be observed showing that the feet temperature is the highest for the heating condition. Such a trend could not reach statistical significance because of the small sample size. In the cooling condition the feet are significantly colder in a narrow time window starting at 3 minutes (see FIG. 29C).

FIGS. 30A-30D depict a change in foot temperature during falling asleep depending on the first temperature setting. Sleep onset was taken as reference. (B) P-value comparing cooling versus heating. Significant values are indicated in orange. (C) P-value comparing Off versus heating. Significant values are indicated in orange. (D) P-value comparing Off versus cooling. Significant values would be identified by blue bars. The change in distal foot temperature taking as reference sleep onset is shown in FIGS. 30A-30D. This time the distal foot temperature in the heating condition is significantly higher than that in the cooling conditions for almost the entire period (FIG. 30B). Distal foot temperature is also significantly lower compared to off in the cooling condition for up to 10 minutes after sleep onset (FIG. 30D). Distal foot temperature is not statistically significant in heating compared to Off.

Wake after sleep onset: The duration of wake after sleep onset (WASO) for each temperature setting is shown in FIGS. 31A-31C. None of the pair-wise comparisons was found to be significant. However, WASO is noticeable higher for the warming setting during the third segment (FIG. 31C). The results of the linear mixed model to characterize the influence of temperature settings on WASO are shown in Table 24. These confirm the absence of statistical significance found in the pairwise comparison analysis. However, the large coefficient 12.6 associated with the 3rd temperature setting suggests an increase in WASO due to warming from 6 to 9 hours after bed entry.

TABLE 24
Linear mixed model to understand the influence
of temperature settings on WASO.

	Coefficients	Std Err.	P

	Intercept	43.06	6.61	0
	1st temp. setting	0.96	9.03	0.92
	2nd temp. setting	−3.73	7.33	0.62
	3rd temp. setting	12.6	8.18	0.13

Association between temperature setting and sleep stages: Linear mixed models were fitted to understand the effect of the temperature setting on deep sleep (see Table 25), light sleep (see Table 26), and REM sleep (see Table 27). No significant effect of the temperature setting were observed on any of Deep sleep, Light sleep, or REM sleep (i.e., all P-values in Table 25 to Table 27 are non-significant).

TABLE 25
Linear mixed model to understand the influence
of temperature settings on deep sleep.

	Coefficients	Std Err.	P

	Intercept	71.7	4.72	0
	1st temp. setting	−1.36	6.44	0.83
	2nd temp. setting	0.13	5.23	0.98
	3rd temp. setting	−2.72	5.84	0.65

TABLE 26
Linear mixed model to understand the influence
of temperature settings on light sleep.

	Coefficients	Std Err.	P

	Intercept	224.2	9.92	0
	1st temp. setting	21.3	13.6	0.126
	2nd temp. setting	11.6	11.0	0.298
	3rd temp. setting	−0.75	12.3	0.952

TABLE 27
Linear mixed model to understand the influence
of temperature settings on REM sleep.

	Coefficients	Std Err.	P

	Intercept	90.1	6.69	0
	1st temp. setting	9.90	9.10	0.285
	2nd temp. setting	8.81	7.39	0.242
	3rd temp. setting	3.97	8.24	0.634

FIG. 32 depicts a distribution of sleep stages vs temperature setting for the first three hours (which coincides with the delivery of the first in-lab temperature setting) across all in-lab sessions. The category sleep results from the sum of Deep+Light+REM.

In general, deep sleep is more prevalent during the first 3 hours of a sleep session. The results show that cooling and heating during the first three hours are significantly associated with longer deep sleep compared to Off.

Compared to Off, cooling and heating are associated with longer (but not significantly) light sleep. Compared to Off, cooling and heating are associated (but not significantly) with shorter amount of wake. The duration of Sleep (i.e. REM+Light+Deep) is significantly longer for the heating compared to Off condition. To better visualize the favorable effect of cooling or heating versus off on deep sleep, the cumulative sleep stage distribution in percent units is presented in FIG. 33 (i.e. REM (Σ)+Light (‡)+Deep (†). FIG. 33 depicts a distribution of percent sleep stages vs temperature setting for the first three hours of in-lab sessions.

FIG. 34 depicts a distribution of sleep stages vs temperature setting for the second three-hour segment (which coincides with the delivery of the second in-lab temperature setting) across all in-lab sessions. Sleep duration in this segment tended (P=0.06) to be longer in the heating compared to the cooling condition.

FIG. 35 depicts a distribution of sleep stages vs temperature setting for the third three-hour segment (which coincides with the delivery of the third in-lab temperature setting) across all in-lab sessions.

Sleep session data: The analysis of sleep session data for the in-lab portion of the study was performed following a similar methodology as the one applied for the in-home analysis, namely using linear mixed models depending on the temperature setting for segments 1 to 3. Since the in-lab conditions are more controlled in terms of bedtime and start of the temperature program, ITT and AT types of analyses are identical. The SIQ score results (Table 28) show that for women, cooling in the second segment (3 to 6 hours) is significantly associated with higher SIQ score. A trending association (P=0.053) was found for cooling and longer sleep duration in the gender agnostic analysis (see Table 29). Longer restful sleep is associated with cooling in the 2nd and 3rd segments for women (see Table 30).

Heart rate changes are not significantly associated with any temperature settings (see Table 31). The gender agnostic heart rate variability analysis (see Table 32) shows a significant association between higher HRV and cooling in the third segment. For women, higher HRV is associated with cooling in the first and third segments. For breathing rate (see Table 33), significant associations were found for lower breathing rate and heating in the first and second segments.

TABLE 28
ITT in-lab analysis - Linear mixed model. SIQ score ~
temperature setting S1 + S2 + S3.

	Coefficients	Std Err.	P

All participants

	Intercept	72.479	2.903	0
	1st temp. setting	0.071	1.03	0.945
	† 2nd temp. setting	−1.984	0.796	0.013
	3rd temp. setting	0.533	0.789	0.5

Men

	Intercept	68.051	5.1	0
	1st temp. setting	0.7	2.015	0.728
	2nd temp. setting	−2.177	1.54	0.157
	3rd temp. setting	0.47	1.274	0.712

Women

	Intercept	76.816	1.862	0
	1st temp. setting	−0.318	0.786	0.686
	† 2nd temp. setting	−1.831	0.819	0.025
	3rd temp. setting	0.059	0.97	0.951

TABLE 29
ITT in-lab analysis - Linear mixed model. Sleep duration ~
temperature setting S1 + S2 + S3.

	Coefficients	Std Err.	P

All participants

	Intercept	7.546	0.154	0
	1st temp. setting	0.003	0.072	0.969
	‡ 2nd temp. setting	−0.118	0.061	0.053
	3rd temp. setting	0.022	0.055	0.686

Men

	Intercept	7.313	0.244	0
	1st temp. setting	0.01	0.154	0.949
	2nd temp. setting	−0.141	0.125	0.258
	3rd temp. setting	0.046	0.077	0.553

Women

	Intercept	7.79	0.162	0
	1st temp. setting	0.018	0.078	0.821
	2nd temp. setting	−0.086	0.087	0.322
	3rd temp. setting	−0.12	0.106	0.256

TABLE 30
ITT in-lab analysis - Linear mixed model. Restful sleep ~
temperature setting S1 + S2 + S3.

	Coefficients	Std Err.	P

All participants

	Intercept	6.625	0.2	0
	1st temp. setting	0.014	0.084	0.867
	2nd temp. setting	−0.088	0.067	0.187
	3rd temp. setting	0.073	0.067	0.272

Men

	Intercept	6.232	0.318	0
	1st temp. setting	0.067	0.163	0.683
	2nd temp. setting	−0.078	0.128	0.542
	3rd temp. setting	0.103	0.101	0.308

Women

	Intercept	6.988	0.082	0
	1st temp. setting	−0.028	0.035	0.423
	† 2nd temp. setting	−0.121	0.029	<1e−4
	† 3rd temp. setting	−0.106	0.052	0.041

TABLE 31
ITT in-lab analysis - Linear mixed model. Heartrate ~
temperature setting S1 + S2 + S3.

	Coefficients	Std Err.	P

All participants

	Intercept	64.339	1.595	0
	1st temp. setting	−0.139	0.659	0.833
	2nd temp. setting	0.1	0.526	0.849
	3rd temp. setting	0.407	0.539	0.45

Men

	Intercept	62.453	2.154	0
	1st temp. setting	0.192	0.657	0.77
	2nd temp. setting	−0.484	0.532	0.363
	3rd temp. setting	−0.095	0.432	0.825

Women

	Intercept	67.035	2.574	0
	1st temp. setting	0.303	1.201	0.801
	2nd temp. setting	0.807	1.101	0.463
	3rd temp. setting	1.825	1.463	0.212

TABLE 32
ITT in-lab analysis - Linear mixed model. HRV ~ temperature
setting S1 + S2 + S3.

	Coefficients	Std Err.	P

All participants

	Intercept	72.39	5.688	0
	1st temp. setting	−1.083	2.88	0.707
	2nd temp. setting	−1.029	2.578	0.69
	† 3rd temp. setting	−6.36	2.753	0.021

Men

	Intercept	74.217	5.735	0
	1st temp. setting	0.475	3.1	0.878
	2nd temp. setting	0.433	2.85	0.879
	3rd temp. setting	−1.535	1.796	0.393

Women

	Intercept	65.612	9.356	0
	† 1st temp. setting	−9.464	3.689	0.01
	2nd temp. setting	0.246	2.148	0.909
	† 3rd temp. setting	−23.472	3.893	<1e−4

TABLE 33
ITT in-lab analysis - Linear mixed model. Breathing rate ~
temperature setting S1 + S2 + S3.

	Coefficients	Std Err.	P

All participants

	Intercept	13.811	0.308	0
	1st temp. setting	−0.189	0.143	0.186
	2nd temp. setting	−0.097	0.103	0.349
	3rd temp. setting	0.016	0.096	0.863

Men

	Intercept	14.036	0.469	0
	1st temp. setting	−0.045	0.192	0.816
	2nd temp. setting	−0.112	0.152	0.464
	3rd temp. setting	−0.02	0.118	0.862

Women

	Intercept	13.427	0.415	0
	† 1st temp. setting	−0.404	0.151	0.007
	† 2nd temp. setting	−0.222	0.096	0.021
	3rd temp. setting	0.356	0.201	0.077

FIG. 36 depicts the results of the foregoing analysis and identifies the four programs that were associated with higher objective sleep quality. FIG. 36 shows the analysis of sleep session data that four temperature programs (surrounded by a green rectangle and denoted by †) are more favorable for sleep quality. These programs are consistent with the results from the foregoing analysis in which generally starting with a warmer microenvironment followed by a cooler one is most beneficial for higher sleep quality.

FIGS. 37A-41 illustrate example user interfaces and user interface flows for an application configured to run on a computing device, such as the user device 2006 illustrated and described in reference to FIG. 20. FIGS. 37A-37F illustrate example user interfaces for configuring a temperature program. FIG. 38 illustrates an example user interface flow for configuring a warming and deep sleep cooling routine. FIG. 39 illustrates an example user interface flow for configuring an all-night cooling routine. FIG. 40 illustrates an example user interface flow for configuring a personalized cooling routine. FIG. 41 illustrates an example user interface flow for customizing a temperature routine.

FIG. 42 illustrates an example process that uses ballistocardiography (BCG) signals from a pressure sensor in a bed system to determine sleep metrics for a user. In some embodiments, a mattress of the bed system includes a pressure sensor that detects temperature in a fluid bladder of a bed system. In some embodiments, a pressure signal from the pressure sensor can be sampled at 500 Hz. Ballistocardiography (BCG) signals, reflecting movement, cardiac activity, and/or respiratory activity, can be obtained by processing the pressure signal, for example as shown in FIG. 42. The smart bed's software and/or hardware as described in the forgoing, can employ machine learning to determine various sleep metrics, including bed presence, bed entry, bed exit, body movements, position changes, time to fall asleep, breathing rate, and heart rate. In some examples, a ballistocardiography signal provides a measure of movement level while the user is lying in bed. For each 10-second window while the user is in bed, the level of movement is compared to a threshold. If the level of activity in a given window is below the threshold, then the given window counts towards restful sleep. In some embodiments, the metrics are uploaded to a server (or any other computer and/or cloud storage system) and aggregated into a feature vector that can be further processed (e.g., using statistical analysis, AI, and/or machine learning.

Optimize Temperature Programs Through Personalization

In some embodiments, a temperature program for a particular user is generated based on an individualized response of the particular user to different temperature settings and timing thereof as observed in previous sleep sessions. Certain temperature conditions of a bed system may help a particular user fall asleep faster, stay asleep longer, reach a deep sleep state faster, stay in a deep sleep state longer, and/or wake up refreshed. In a bed system with heating and/or cooling features the temperature of the bed system can be modulated according to a temperature program that is optimized to improve the particular user's sleep quality. In some examples, a model can be configured to predict a response of the particular user to different temperature settings for one or more segments of a sleep session. This prediction can be used to generate a temperature program that promotes improved sleep quality for the particular user.

In some examples, historical data from a user includes one or more sleep sessions each of which can be segmented into multiple segments. The multiple segments can include a first segment for preconditioning a bed, a second segment for falling asleep, a third segment for staying asleep, and a fourth segment for waking up. For each of these segments, the historical data includes an indication of what temperature setting was applied at the bed system where the user is sleeping. The historical data also includes sleep metrics for the user for each sleep session. These metrics can include a time to fall asleep, a sleep duration, a sleep quality score, heart rate, heart rate variability, and breathing rate. In some examples, a model (e.g., a linear model) is configured using the historical data for the particular user to generate an output indicating a prediction of temperature settings for each of the segments of the temperature program that would improve the sleep quality for the user. In some examples, a model (e.g., a machine learning model) is trained, using the historical data, to predict an optimized temperature program for the particular user.

In some examples, there may not be sufficient historical data for a particular user. For example, when the user is a new user or when the historical data for the particular data is too inconsistent to arrive at a significant result. In these examples, the particular user can be compared to a plurality of other users with temperature programs to identify a most similar user. The temperature program for the most similar user can be selected for use by the particular user. Similarity between users can be determined based on demographic characteristics and/or sleep behavior characteristics. Examples of demographic characteristics include age, gender, weight, height, etc. Examples of sleep behavior characteristics include regularity of sleep patterns, bed time, wakeup time, personal chronotype (e.g., subjective preference for morning or evening hours). In some examples, the individual response of the new user is then evaluated over one or more sleep sessions starting with the selected temperature program to collect enough data (e.g., the historical data) to generate an individualized program for the particular user.

FIG. 43 illustrates an example flow diagram illustrating a method 4300 for generating a model for optimizing a temperature program at an individual level. In some examples, the method 4300 is performed by a computing system. The computing system can include storage hardware for a database storing the historical data 4302, one or more processors, and a memory storing instructions that cause the computing system to perform the method 4300.

The method 4300 can include a step for receiving historical data 4302 capturing one or more sleep sessions for a particular user. The historical data can include sleep data for the particular user for each of the one or more sleep sessions and temperature setting data for each of the one or more sleep sessions. The temperature setting data can include, for each sleep session of the one or more sleep sessions, an indication of a temperature setting applied to the particular user during one or more segments in the sleep session. The method 4300 can include a step for providing the sleep data and the temperature setting data for each of the one or more sleep sessions as inputs to a model that generates an output indicative of a response of the particular users to temperature settings for the one or more segments of a predicted sleep session. The method 4300 can include steps for generating a temperature program that promotes improved sleep quality for the particular user based on the output and configuring a temperature control system to alter a temperature of at least a portion of a bed system according to the temperature program.

In this example, the historical data 4302 (e.g., including recorded sleep sessions with temperature-setting usage) exists for a user and is stored in a database. The historical data can include data for multiple sleep sessions of the particular user. For each considered sleep session in the historical data 4302, four segments are defined using sleep onset time (as referenced at 4304) that characterize the temporal usage of temperature settings. In one example, the first segment spans from 1 hour to 0.5 hours before sleep onset and is considered as preparatory (e.g., in cold climates, some users would pre-heat the bed to enhance their in-bed experience), the second segment spans from 0.5 hours before sleep onset to 1.5 hours after sleep onset, the third segment spans from 1.5 hours to 4.5 hours after sleep onset and is associated with staying asleep, and the fourth segment lasts from 4.5 hours after sleep onset until wakeup. In some examples, the second segment may have the highest influence on the falling asleep period. In some examples, the fourth segment has the highest influence on the wakeup experience. In some embodiments, a different number of segments can be used (e.g., 1 segment, 2 segments, 3 segments, 5 segments, 10 segments, etc.). For example, the segments can include four segments, including a first segment for preconditioning the bed system, a second segment for falling asleep, a third segment for staying asleep, and a fourth segment for waking up. In another example, the segments can include three segments, including a first segment for falling asleep, a second segment for staying asleep, and a third segment for waking up. In some examples, the segments are defined based on a predetermined time relative to sleep onset. In other examples, the segment durations can be individualized based on the sleep data for the particular user.

In addition to the temperature setting (TS1, TS2, TS3, TS4) for each of the four or more segments, the historical data can include sleep data for the particular user for multiple sleep sessions. Examples of metrics included in the sleep data include sleep duration, restful sleep duration, a sleep quality score, a time to fall asleep duration, heart rate data, heart rate variability data, breathing rate data, or any combination thereof are also available for each considered sleep session (see 4306). This data can be collected using one or more sensors at the bed system of the user.

In the example figure shown, the temperature setting data is encoded numerically as shown at 4308. In some examples, the encodings are stored in the historical data for each segment. In some examples, the encodings are assigned prior to providing the temperature setting data as an input to the model. In this figure, the encoding assumes a linear scale from high cooling (−3) to high heating (+3). However, other encoding schemes could alternatively be used. For example, the historical data for the particular user can be processed using dummy encoding or one-hot encoding to identify user specific encodings for each of the temperature settings. In another example, the historical data for a particular population can be processed using dummy encoding or one-hot encoding to identify non-linear encodings for each of the temperature settings. An advantage of using the population to identify the encodings includes the availability of a large dataset making it more likely to identify significant results in cases where only limited data for the particular user exists.

In the example shown, for each sleep session a sleep score (from the sleep data) and the encodings for the temperature setting at each segment in the sleep session (from the temperature setting data) are considered to build a linear model, represented by the equation 4310, at step 4312. In the equation 4310, the sleep quality data is the sleep score (however, a different metric or combination of metrics that relate to sleep quality can be used in other embodiments), β0 is the intercept which, in this example, is set to an average sleep score for the user. The other β coefficients (β1, β2, β3, β4) define the personalized temperature program at step 4314. In the equation shown there are four segments that each include a temperature setting (TS1, TS2, TS3, TS4). The sleep score and corresponding encoded temperature setting data are processed (e.g., by fitting the model with data from the targeted user at step 4312) to solve for the β coefficients 5314 (β1, β2, β3, β4) and p values for each of the β coefficients (β1, β2, β3, β4).

The β coefficients (β1, β2, β3, β4) and corresponding p values are processed to define the personalized temperature program at step 4314. For example, each of the β coefficients can correspond to a segment of the personalized temperature program. A p value for a β coefficient that is not considered significant (e.g., greater than 0.05) may indicate that the off temperature setting should be assigned to the corresponding segment, a negative β coefficient may indicate that a cooling mode should be assigned to the corresponding segment, and a positive β coefficient may indicate that a heating mode should be assigned to the corresponding segment. In some examples, the intensity of the heating or cooling mode is determined based upon the magnitude of the β coefficient. For example, a β coefficient with a relatively high absolute magnitude can be indicative for using a low intensity heating or cooling setting (e.g., low heating or low cooling depending on the sign of the coefficient) and a β coefficient with a relatively low absolute magnitude can be indicative for using a high intensity for heating or cooling setting (e.g., high heating or high cooling depending on the sign of the coefficient). The intensity of the heating or cooling can also be based on the p value for the coefficient. For example, the high intensity modes (high cooling or high heating) may only be selected when the p value is very low (e.g., indicating that the correlation is very significant).

In the example shown, a linear model (see equation 4310) is used. However, other models (including non-linear ones) can be used in other embodiments. In some examples, the model is a regression model. In the example embodiment shown, the sleep score is the target sleep metric, however, other metrics such as restful sleep can be used in a similar manner.

In some examples, the number of sleep sessions with temperature setting usage in the historical data needed to reliably configure the model is ten. However, other embodiments may use a different number of sessions, for example, depending on the number of segments or type of model being used. In some examples, the model is continuously and/or periodically configured as more data for a particular user is accumulated. In some examples, the model and the temperature program can be updated on a periodic basis (e.g., on a weekly, bi-weekly, monthly, yearly basis). In some examples, the historical data uses a rolling window of the most recently recorded sleep sessions. An advantage of updating the model with recorded sleep sessions in a rolling window includes allowing the temperature program to update to capture changes in the users response that are based on cofounding variables. For example, a user's response to a temperature program may vary by the meteorological season (e.g., resulting in changes in daylight, humidity, etc.), aging, changes in light exposure, and/or physiological/health changes. The temperature program can automatically adjust to improve the sleep quality for the user.

In some examples, the historical data can be filtered to remove sleep sessions with bad or noisy sleep data. For example, sleep sessions where the sleep onset time that is outside of a standard deviation can be removed prior to fitting the model (e.g., because a user is more likely to experience bad sleep if they go to bed later than normal). In another example, sessions with an unusual sleep pattern and/or duration can be filtered out before fitting the model. For example, all sleep sessions with a duration of under four hours can be filtered out of the training dataset. In some examples, a user can enter an indication (e.g., via a computer application) that the user experienced an adverse effect for the sleep session that was unrelated to the temperature program (e.g., due to an illness, injury, unrelated adverse stimulus such as noise, etc.).

FIG. 44 illustrates a table 4400 presenting an example output for the model illustrated and described in FIG. 43. In this example, the significant coefficients (e.g., the coefficients with a corresponding p value that is less than 0.05) include the first temperature setting, the second temperature setting, and the fourth temperature setting. These example results indicate that for this particular user the best temperature program consists of warming in the first segment (+3.75 points to the SIQ score), followed by cooling in the second segment ((−1)×(−3.09) points to the SIQ score), and cooling in the fourth segment ((−1)×(−1.81) points to the SIQ score).

The temperature setting in the third segment may trend to warming. However, this result includes a p score indicating that the result may not be significant (e.g., a p score that is greater than 0.05). In some embodiments, the setting of the third segment in this example is set to warming because the corresponding coefficient indicates that this would improve the user's sleep score. In other embodiments, the third segment may be set to a cooling setting (or a different heating setting) in subsequent iterations because the calculated lack of statistical significance of the results may indicate the need for exploration to further refine the model and identify a more significant result for the temperature setting. For example, when the significance score for a coefficient for a particular segment in the one or more segments is less than a threshold, the temperature setting in the temperature program is changed to explore for a temperature setting for the particular segment that improves the sleep quality for the particular user. In some examples, when the p value indicates a result is not significant, the temperature program could assign the segment the “off” temperature setting, for example, to reduce energy consumption and wear of parts.

FIG. 45 illustrates an example flow diagram illustrating a method 4500 for identifying a temperature program for a targeted user 4502. In some examples, the method 4500 is used when the targeted user 4502 has an insufficient number of sleep sessions recorded in the historical data. In some examples, the method 4500 is used when the system determines that the historical data for the targeted user 4502 is too inconsistent to arrive at a significant result. In these examples, the targeted user 4502 can be compared to a plurality of other users with temperature programs to identify a most similar user 4508 and the temperature program for the most similar user can be selected for use by the particular user.

In some examples, similarity between users can be determined based on demographic characteristics, sleep metrics, and/or sleep behavior characteristics. Examples of demographic characteristics include age, gender, weight, height, etc. Examples of sleep metrics include sleep duration, duration of restful sleep, heart rate during sleep, breathing rate during sleep. Examples of sleep behavior characteristics include regularity of sleep patterns, bed time, wakeup time, personal chronotype, etc. In some examples metrics for chronotype and sleep regularity are calculated and used to compare the users. These metrics can be derived from sensors in the bed system. In some examples, the chronotype and/or regularity metrics are calculated using a pressure signal from a pressure sensor in air bladder of a bed system.

In some examples, the individual response of the targeted user is then evaluated over one or more sleep sessions starting with the selected temperature program to collect enough data (e.g., the historical data) to generate an individualized program for the particular user. In some examples, after the targeted user 4502 accumulates sufficient data (e.g., historical data including sleep data and temperature setting data), the approach illustrated in FIG. 43 can be applied.

In the example shown, data for the targeted user 4502 is compared to data for existing users 4504 that have already been configured with personalized temperature programs. The temperature program(s) associated with the most similar users is (are) selected for the targeted user 4502. A similarity metric 4506 can be calculated between the targeted user 4502 and each of the existing users 4504 with personalized temperature programs based on characteristics of the targeted users 4502 and of the existing users 4504 with known temperature programs. In some examples, the similarity metric can be determined based on a comparison of demographic characteristics (age group and gender) and/or sleep behavior characteristics (e.g. chronotype and sleep regularity). In some examples, a similarity search is performed first to determine which existing user with known temperature program(s) is most similar to the targeted user 4502 (e.g., the most similar user 4508). A temperature program for the most similar user 4508 can be retrieved from a database 4510 storing the temperature programs for the existing users 4504. In the example shown, the temperature program is recommended 4512 to the targeted user 4502 (e.g., via a computer application) for implementation at the bed system for the targeted user 4502. In some examples, the temperature program is automatically implemented at the bed system for the target user 4502 (e.g., when an automatic temperature program selection setting is enabled).

In some embodiments, the similarity score between two users is determined using cosine similarity. For example, each user can be represented with a vector with components. For, example, a vector may include values for gender (−1,0,1), age, weight, height, chronotype (1: morning, 0 no preference, −1 evening), and regularity. The similarity score between two users can be quantified by calculating the dot product between the corresponding feature vectors divided by the norm of the vectors.

In some examples, characteristics for the targeted user 4502 is obtained by having the targeted user 4502 complete a questionnaire. For example, the questionnaire can be used to obtain demographic information and/or sleep characteristics for the targeted user 4502. In some examples, the characteristics can include characteristics derived from measurements of one or more sensors in the bed system. For example, sensors can be configured to calculate a weight for a user, a sleep time, a wake up time, sleep duration, resultful seep duration, an average time to fall asleep, an average sleep score, heart rate, breathing rate, heart rate variability, and/or a chronotype for the user. These metrics, individually or in any combination, can be used to identify the most similar user 4504 to the targeted user 4502.

Example 2

The objective of Example 2 was to evaluate the effect of smart temperature programs on restful sleep duration. Each program consisted of three temperature settings applied throughout a sleep session. Three programs were designed: one involving continuous cooling across the night, one applying cooling during deep sleep, and one combining initial warming with subsequent cooling. Analyses were limited to weekday nights to reduce confounding by weekend sleep extension. Wilcoxon paired tests and effect sizes were used to assess differences.

Table 34 shows the smart temperature programs evaluated during the winter months (October to January). The analysis was performed to assess whether use of the temperature programs was associated with differences in restful sleep duration compared to the OFF condition.

TABLE 34
Smart
Program	1st segment	2nd segment	3rd segment
P1) All night	Low cooling	Medium cooling	Low cooling
cooling	(IBT decrease	(IBT decrease
	by −1° C.)	by −2° C.)
P2) Deep sleep	Off	Medium cooling	Low cooling
cooling
P3) Warming	Low heating	Medium cooling	Low heating
and deep sleep	(IBT increase
cooling	by +1° C.)

FIG. 46 illustrates that activation of the smart temperature programs increased the duration of restful sleep relative to the OFF condition during the winter months. The “Warm deep sleep cool” program produced the largest average gain (+52 minutes), followed by “All night cooling” (+27 minutes), “Deep sleep cooling” (+13 minutes), and “Personal” (+11 minutes).

Table 35 shows the average restful sleep duration for each program (ON) compared to the OFF condition, along with the difference in minutes, statistical significance, and effect size. For example, the Warm deep sleep cool program showed an average of 8:03 hours of restful sleep versus 7:10 hours with OFF, yielding a 52-minute increase (p<10⁻⁶, effect size 0.556).

TABLE 35
					Effect
Program	ON	OFF	Diff	p-value	size

Warm deep sleep cool	8:03	7:10	0:52	<10−6	0.556
All night cooling	7:30	7:03	0:27	<10−6	0.306
Deep sleep cooling	7:24	7:10	0:13	<10−6	0.123
personal	7:21	7:09	0:11	<10−6	0.126

Table 36 shows the number of users contributing data for each program, along with the total number of smart temperature program (STP) sessions and matched OFF sessions. The user counts are program-specific, meaning an individual may be represented in more than one program if they used multiple settings. For example, 6,130 users generated 43,685 STP sessions and 78,253 OFF sessions for the Warm deep sleep cool program, while 57,388 users contributed over 1.2 million STP sessions and 1.6 million OFF sessions under the Personal program.

TABLE 36
Program	user count	STP session	OFF session

Warm deep sleep cool	6130	43685	78253
All night cooling	26894	567720	550062
Deep sleep cooling	12011	35995	308483
Personal	57388	1217208	1669647

Together, FIG. 46, Table 35, and Table 36 show that both the magnitude of the effects of each program on restful sleep and the breadth of the dataset analyzed, spanning tens of thousands of users and millions of sleep sessions.

Table 37 shows the number of users across eight demographic groups who engaged with the “Warming and deep sleep cooling” (WDC) program. The table distinguishes participants by both age segment and gender, and lists the number of users for each group together with the corresponding counts of WDC sessions and matched OFF sessions. For example, among users aged 35-54 years, 2,102 women contributed 42,213 WDC sessions and 25,738 OFF sessions, while 1,403 men generated 32,152 WDC sessions and 17,419 OFF sessions.

Table 38 presents the effects of WDC on restful sleep duration in the same eight demographic groups defined in Table 37. Across all groups, WDC use was associated with increased restful sleep, with observed gains ranging from approximately 49.7 minutes (men aged 55-74 years) to 60.5 minutes (both men and women aged 18-34 years). For the full population, WDC was associated with a 52.5-minute increase compared to OFF. Effect sizes ranged from 0.49 to 0.68, with the largest observed effect in men aged≥75 years (effect size 0.68). All reported results were statistically significant (p<10⁻⁶).

Together, Tables 37 and 38 demonstrate that the WDC program consistently prolonged restful sleep across diverse demographic segments.

TABLE 37
Age segment	Gender	User count	STP session	OFF Session

18-34 years	F	266	5865	3000
	M	192	3463	1979
35-54 years	F	2102	42213	25738
	M	1403	32152	17419
55-74 years	F	1012	20451	12824
	M	885	18062	12394
≥75 years	F	51	1222	854
	M	88	1631	1550

TABLE 38
	Restful sleep	Restful sleep	Difference	Statistical
	duration	duration	(WDC −	signif-
	OFF	WDC	OFF)	icance	Effect
Segment	[minutes]	[minutes]	[minutes]	(p)	size

All	431.0	483.5	52.5	<10−6	0.58
	(98.2)	(98.2)
18-34	455.8	516.3	60.5	<10−6	0.50
years (F)	(90.3)	(97.0)
18-34	436.8	497.3	60.5	<10−6	0.55
years (M)	(102.7)	(92.9)
35-54	447.1	500.5	53.3	<10−6	0.65
years (F)	(90.2)	(102.5)
35-54	413.9	473.5	59.6	<10−6	0.62
years (M)	(81.7)	(92.6)
55-74	443.1	492.8	49.7	<10−6	0.49
years (F)	(99.9)	(105.0)
55-74	414.0	473.8	59.8	<10−6	0.64
years (M)	(85.8)	(99.1)
≥75	466.0	517.2	51.3	<10−6	0.55
years (F)	(107.0)	(95.7)
≥75	432.9	486.7	53.8	<10−6	0.68
years (M)	(90.2)	(105.5)

Table 39 shows the number of users across eight demographic groups who engaged with the “All night cooling” (ANC) program. The table separates participants by age segment and gender, and provides the number of users for each group along with the counts of ANC sessions and matched OFF sessions. The user counts are program-specific, so individuals may appear in more than one program if they used multiple settings. For example, in the 35-54 year age range, 6,942 women contributed 136,075 ANC sessions and 142,812 OFF sessions, while 7,799 men contributed 175,287 ANC sessions and 153,864 OFF sessions.

Table 40 shows the observed effects of ANC on restful sleep duration for the same eight demographic groups defined in Table 39. Across all users, ANC was associated with an average increase of restful sleep by an average of 27.5 minutes compared to OFF (p<10⁻⁶, effect size 0.31). The gains varied by demographic group, ranging from approximately 21.4 minutes in men≥75 years (effect size 0.21) to 36.4 minutes in men aged 18-34 years (effect size 0.41). Effect sizes were generally small to moderate, with the largest observed effects observed in younger men and women aged 35-54 years (effect size 0.42).

Together, Tables 39 and 40 indicate that ANC provides consistent improvements in restful sleep across demographic segments.

TABLE 39
Age segment	Gender	User count	Session count	Off count

18-34 years	F	866	14228	18354
	M	970	20293	18757
35-54 years	F	6942	136075	142812
	M	7799	175287	153864
55-74 years	F	4596	97076	93744
	M	4673	95315	100564
≥75 years	F	284	5646	6555
	M	338	6419	8343

TABLE 40
	Restful sleep	Restful sleep	Difference	Statistical
	duration	duration	(ANC −	signif-
	OFF	ANC	OFF)	icance	Effect
Segment	[minutes]	[minutes]	[minutes]	(p)	size

All	423.1	450.6	27.5	<10−6	0.31
	(89.6)	(90.1)
18-34	454.9	480.2	25.3	<10−6	0.28
years (F)	(86.8)	(91.8)
18-34	421.2	457.6	36.4	<10−6	0.41
years (M)	(87.8)	(89.8)
35-54	442.8	471.1	28.3	<10−6	0.31
years (F)	(89.3)	(93.2)
35-54	406.7	440.7	34.0	<10−6	0.42
years (M)	(79.8)	(81)
55-74	440.8	465.6	24.9	<10−6	0.26
years (F)	(93.9)	(95)
55-74	411.6	436.3	24.7	<10−6	0.29
years (M)	(85.3)	(86.5)
≥75	461.8	486.8	24.9	<10−6	0.23
years (F)	(102.4)	(114.2)
≥75	437.9	459.4	21.4	<10−6	0.21
years (M)	(99.1)	(106.1)

Table 41 shows the number of users across eight demographic groups who engaged with the “Deep sleep cooling” (DSC) program. The table separates participants by age segment and gender, and provides the number of users for each group together with their corresponding DSC sessions and matched OFF sessions. The user counts are program-specific, and the same individual may be represented in more than one program if they engaged with multiple settings. For example, among users aged 35-54 years, 3,582 women generated 11,261 DSC sessions and 90,138 OFF sessions, while 2,905 men contributed 7,898 DSC sessions and 72,799 OFF sessions.

Table 42 shows the observed effects of DSC on restful sleep duration for the same eight demographic groups defined in Table 41. Across all users, DSC was associated with an average increase of restful sleep by an average of 11.7 minutes compared to OFF (p<10⁻⁶, effect size 0.12). The observed changes varied by demographic group, ranging from 7.5 minutes in women aged 55-74 years (effect size 0.07) to 14.5 minutes in men aged≥75 years (effect size 0.14). Statistical significance was strong for most groups (p<10⁻⁶ to 0.03), though the effect in women≥75 years did not reach significance (p=0.08).

Together, Tables 41 and 42 demonstrate that DSC produced modest but consistent gains in restful sleep across demographic segments.

TABLE 41
Age segment	Gender	User count	On session	Off_session

18-34 years	F	431	1057	11546
	M	359	779	8296
35-54 years	F	3582	11261	90138
	M	2905	7898	72799
55-74 years	F	2189	7007	56321
	M	1855	5527	49772
≥75 years	F	120	284	3565
	M	109	356	3163

TABLE 42
	Restful sleep	Restful sleep	Difference	Statistical
	duration	duration	(DSC −	signif-
	OFF	DSC	OFF)	icance	Effect
Segment	[minutes]	[minutes]	[minutes]	(p)	size

All	429.7	441.5	11.7	<10−6	0.12
	(89.8)	(96.2)
18-34	461.3	473.2	11.8	0.02	0.11
years (F)	(82.5)	(125.9)
18-34	429.8	441.2	11.3	0.03	0.10
years (M)	(88.9)	(125.2)
35-54	446.9	459.4	12.5	<10−6	0.12
years (F)	(84.8)	(119.9)
35-54	415.3	434.7	19.4	<10−6	0.18
years (M)	(81.7)	(124.2)
55-74	444.6	452.1	7.5	<10−6	0.07
years (F)	(93.8)	(130.1)
55-74	412.9	423.4	10.5	<10−6	0.10
years (M)	(86.4)	(118.9)
≥75	472.9	486.7	13.8	0.08	0.09
years (F)	(128.5)	(167)
≥75	442.6	457	14.5	0.03	0.14
years (M)	(92.1)	(119.3)

FIG. 47 shows the effect of the smart temperature programs (STP) on restful sleep duration in users who self-reported insomnia. The bar chart shows that all four program types (X-axis) were associated with longer restful sleep duration (Y-axis) compared to the OFF condition, with the largest observed change for the “Warm deep sleep cooling” program (+1:09 hours).

Table 43 shows the measured restful sleep duration values under STP and OFF for each program, together with the calculated difference, p-value, and effect size. For example, the Warm deep sleep cooling program was associated with a 1 hour 9 minute longer restful sleep duration compared to OFF (p=0.00, effect size 1.03). The other programs showed smaller observed numerical increases, ranging from 15 to 46 minutes, with corresponding effect sizes between 0.14 and 0.40.

Table 44 shows the number of users with insomnia who contributed data for each program, along with the number of STP and OFF sessions. The user counts are program-specific, and individuals may appear in more than one program if they engaged with multiple settings. For example, 29 users contributed 739 STP sessions and 276 OFF sessions for the All night cooling program, while 61 users contributed 1,620 STP sessions and 1,272 OFF sessions for the Personal program.

Together, FIG. 47, Table 43, and Table 44 show the observed data for restful sleep in insomnia users across the four program types.

TABLE 43
	STP	OFF	Difference		Effect
Program	(h:mm)	(h:mm)	(h:mm)	p-value	size

Warm deep sleep cool	8:34	7:24	1:09	0.00	1.03
All night cooling	8:04	7:22	0:41	0.06	0.36
Deep sleep cooling	8:45	7:58	0:46	0.17	0.40
Personal	7:57	7:41	0:15	0.24	0.14

TABLE 44
Program	Sleeper count	STP sessions	OFF sessions

Warm deep sleep cool	11	85	78
All night cooling	29	739	276
Deep sleep cooling	11	42	223
Personal	61	1,620	1,272

FIG. 48 depicts restful sleep duration for users who self-report sleep apnea under four smart temperature programs (STP) compared to the OFF condition. The bars indicate the measured duration of restful sleep (Y-axis) for each program (X-axis) when ON versus OFF.

Table 45 shows the observed effects of measured restful sleep durations under STP and OFF, the calculated differences, p-values, and effect sizes. The Warm deep sleep cooling program showed the largest numerical observed increase in restful sleep duration, with smaller gains observed for All night cooling, Deep sleep cooling, and Personal.

Table 46 presents the number of apnea users who contributed data for each program, together with the counts of STP and OFF sessions. The user counts are program-specific, and individuals may appear in more than one program if they engaged with multiple settings. For example, 99 users contributed 2,335 STP sessions and 1,258 OFF sessions for the All night cooling program.

Together, FIG. 48, Table 45, and Table 46 provide the observed data for apnea users under smart temperature programs and OFF.

TABLE 45
	STP	OFF	Difference		Effect
Program	(h:mm)	(h:mm)	(h:mm)	p-value	size

Warm deep sleep cool	8:04	6:52	1:11	0.01	0.59
All night cooling	7:11	6:52	0:19	0.07	0.23
Deep sleep cooling	7:16	6:53	0:23	0.13	0.24
Personal	7:06	6:56	0:10	0.14	0.11

TABLE 46
Program	Sleeper count	STP sessions	OFF sessions

Warm deep sleep cool	40	392	452
All night cooling	99	2,335	1,258
Deep sleep cooling	46	138	769
Personal	180	5,078	4,394

FIG. 49 shows restful sleep duration for users who self-reported chronic pain under four smart temperature programs (STP) (X-axis) compared to the OFF condition. The chart shows the measured restful sleep durations (Y-axis) for each program when ON versus OFF.

Table 47 shows the observed effects of measured restful sleep durations under STP and OFF, the calculated differences, p-values, and effect sizes. The Warm deep sleep cooling program showed the largest numerical increase in restful sleep duration (56 minutes, p=0.02, effect size 0.71).

Table 47 also shows the number of chronic pain users who contributed data for each program, together with their corresponding STP and OFF session counts. The user counts are program-specific, and individuals may appear in more than one program if they engaged with multiple settings. For example, 80 users contributed 1,767 STP sessions and 1,308 OFF sessions for the All night cooling program, while 143 users contributed 3,303 STP sessions and 3,705 OFF sessions for the Personal program.

TABLE 47
	STP	OFF	Difference	p-	Effect	User
Program	(h:mm)	(h:mm)	(h:mm)	value	size	Count	ON	OFF

Warm	7:57	7:01	0:56	0.02	0.71	17	173	95
deep
sleep
cool
All night	7:38	7:07	0:31	0.03	0.29	80	1767	1308
cooling
Deep	7:37	7:25	0:12	0.27	0.11	46	147	938
sleep
cooling
Personal	7:30	7:23	0:06	0.32	0.06	143	3303	3705

FIG. 50 depicts restful sleep duration for users reporting chronic stress under four smart temperature programs (STP) compared to the OFF condition. The chart shows the measured restful sleep durations (Y-axis) for each program (X-axis) when ON versus OFF.

Table 48 shows the observed effects of measured restful sleep durations under STP and OFF, along with the calculated differences, p-values, and effect sizes. The Warm deep sleep cooling program showed the largest observed numerical increase in restful sleep duration (53 minutes, p=0.02, effect size 0.82). All night cooling and Deep sleep cooling were associated with increases of 35 minutes and 11 minutes, respectively, while the Personal program showed a 24-minute increase.

Table 48 also shows the number of chronic stress users who contributed data for each program, together with the counts of STP and OFF sessions. The user counts are program-specific, and individuals may appear in more than one program if they engaged with multiple settings. For example, 38 users contributed 772 STP sessions and 766 OFF sessions for the All night cooling program, while 63 users contributed 1,728 STP sessions and 1,515 OFF sessions for the Personal program.

Together, FIG. 50 and Table 48 show the observed data for chronic stress users under the four STP settings and OFF.

TABLE 48
	STP	OFF	Difference	p-	Effect	User
Program	(h:mm)	(h:mm)	(h:mm)	value	size	Count	ON	OFF

Warm	7:30	6:37	0:53	0.02	0.82	14	136	179
deep
sleep
cool
All night	7:35	6:59	0:35	0.10	0.31	38	772	766
cooling
Deep	7:15	7:03	0:11	0.34	0.13	24	65	538
sleep
cooling
Personal	7:23	6.58	0.24	0.08	0.25	63	1728	1515

FIG. 51 shows restful sleep duration for users self-reporting depression under four smart temperature programs (STP) compared to the OFF condition. The chart shows the measured restful sleep durations (Y-axis) for each program (X-axis) in the ON and OFF states.

Table 49 shows the observed effects of measured restful sleep durations, the differences between STP and OFF, and the associated p-values and effect sizes. Warm deep sleep cooling and Deep sleep cooling each showed a 1 hour 6 minute increase compared to OFF, with effect sizes of 1.02 and 0.84, respectively.

Table 49 also shows the observed number of depression users who contributed data, along with their corresponding STP and OFF session counts. The user counts are program-specific, and individuals may appear in more than one program if they engaged with multiple settings. For example, 19 users contributed 302 STP sessions and 374 OFF sessions for All night cooling, while 31 users contributed 822 STP sessions and 780 OFF sessions for the Personal program.

TABLE 49
	STP	OFF	Difference	p-	Effect	User
Program	(h:mm)	(h:mm)	(h:mm)	value	size	Count	ON	OFF

Warm	7:38	6:32	1:06	0.18	1.02	4	39	68
deep
sleep
cool
All night	7:20	6:38	0:41	0.07	0.48	19	302	374
cooling
Deep	7:40	6:34	1:06	0.06	0.84	8	21	218
sleep
cooling
Personal	7:24	6:54	0:29	0.12	0.30	31	822	780

Results: In the overall population of 44,017 users (22,441 women and 21,576 men; mean age 51.1±12.7 years), all STP settings were associated with longer restful sleep compared to OFF. The largest observed difference was for the Warming and deep sleep cooling program, with an average increase of approximately 52 minutes. In the 18-34 year age segment, this program was associated with an increase of about 60.5 minutes. The All night cooling program showed an average increase of 27 minutes, while the Deep sleep cooling program showed an average increase of 11 minutes. Custom programs were associated with an average increase of 12 minutes.

Subgroup analyses in individuals with self-reported conditions showed the following: For users with insomnia and sleep apnea, the STP settings were associated with gains of roughly one hour in restful sleep duration. For users reporting chronic conditions, observed increases in restful sleep duration were about 56 minutes for chronic pain and 53 minutes for chronic stress.

The foregoing detailed description and some embodiments have been given for clarity of understanding only. No unnecessary limitations are to be understood therefrom. It will be apparent to those skilled in the art that many changes can be made in the embodiments described without departing from the scope of the invention. For example, a different order and type of operations may be used to generate classifiers. Additionally, a bed system may aggregate output from classifiers in different ways. Thus, the scope of the present invention should not be limited to the exact details and structures described herein, but rather by the structures described by the language of the claims, and the equivalents of those structures. Any feature or characteristic described with respect to any of the above embodiments can be incorporated individually or in combination with any other feature or characteristic, and are presented in the above order and combinations for clarity only.

A number of embodiments of the inventions have been described. Nevertheless, it will be understood that various modifications can be made without departing from the spirit and scope of the invention. For example, in some embodiments the bed need not include adjustable air chambers. Moreover, in some embodiments various components of the foundation 600 can be shaped differently than as illustrated. Additionally, different aspects of the different embodiments of foundations, mattresses, and other bed system components described above can be combined while other aspects as suitable for the application. Accordingly, other embodiments are within the scope of the following claims.