Patent Application
20240065559
The present document relates to systems, methods, and techniques for automatically determining biometric information about a user of a bed system, such as heartrate and respiration rate, while the user rests on the bed system and based on automated processing of load-cell signals received from sensors at the bed system.
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.
This document generally describes technology for determining biometric information about a user of a bed system based on processing load-cell signals or other force signals generated by sensors of the bed system. The bed system can include load cells (e.g., force sensors) mounted to one or more legs of a base, frame, or foundation of the bed system. The load cells can collect load-cell signals or other force signals that are detected when the user/sleeper rests on the bed system. The disclosed technology provides for receiving the load-cell signals from at least one of the load cells and processing the received load-cell signals to determine the user's heartrate and/or respiration rate during a sleep session (and/or when the user is detected as resting on the bed system). In some implementations, the disclosed technology can receive load-cell signals from one load cell that is nearest a head end of the bed system, since biometrics (e.g., heartrate and respiration rate) can be more prominently detected closer to a head of the user rather than feet of the user. Sometimes, load-cell signals from more than one load cell can be combined to improve accuracy in biometric information determinations.
The disclosed technology may also be used to determine other biometric information about the user, including but not limited to heartrate variability (HRV). One or more rules and/or algorithms can be applied to the received load-cell signals to determine the user's respiration rate at predetermined time intervals during the sleep session and/or in the aggregate for an entire duration of the sleep session. Additionally or alternatively, one or more machine learning-trained models can be applied to the received load-cell signals to determine the user's heartrate at predetermined time intervals during the sleep session and/or in the aggregate for the entire duration of the sleep session.
In some implementations, the disclosed technology can apply a variety of rules, algorithms, and/or machine learning-trained models to the same load-cell signals to determine (e.g., estimate) different biometric parameters (e.g., heartrate, respiration rate, HRV) of the user throughout or during the user's sleep session. The rules, algorithms, and/or machine-learning trained models that are applied can vary depending on sensitivity of signal acquisition needed for each biometric parameter. For example, a user's breathing patterns can be detected more easily from raw load-cell signals than their beating heart patterns. Therefore, the disclosed technology can apply one or more filtering rules to the load-cell signals to determine the respiration rate. The applied rules can include a zero-cross detection process, in which instances when the filtered load-cell signals crosses a y axis (e.g., signal amplitude) indicate changes in a breathing pattern of the user (e.g., inhales and exhales). Although the zero-cross detection process is described herein, another vertical threshold value can be used for the cross detection process other than zero. That vertical threshold value can vary depending on a particular use case/implementation. The disclosed technology can also apply a machine learning-trained model to the same load-cell signals to determine the heartrate. The model can be trained to associate the raw load-cell signals with heartrate values.
Some embodiments described herein include a bed system including: a support element having at least one leg, at least one force sensor of the at least one leg, the force sensor being configured to sense a force applied to the bed system or the leg, and a controller that can be configured to: receive at least one force data-stream from the at least one force sensor, the at least one force data-stream representing a force sensed by the force sensor, determine a biometric parameter of a user on the bed system at predetermined time intervals based on processing the at least one force data-stream, generate an aggregate biometric parameter of the user based on aggregating the biometric parameters for the predetermined time intervals, and return the aggregate biometric parameter of the user.
Embodiments described herein can include one or more optional features. For example, the at least one force sensor can be a load-cell. The biometric parameter can be a heartrate of the user. The biometric parameter can be a respiration rate of the user. The at least one leg having the at least one force sensor can be positioned near a head portion of the bed system. The support element further can include a second leg having a second force sensor. The controller can be configured to: receive a second force data-stream from the second force sensor and determine the biometric parameter of the user at the predetermined time intervals based on processing the at least one force data-stream and the second force data-stream. The controller may also detect a presence of the user on the bed system, identify, based on the detected user presence on the bed system, one of the at least one force sensor and the second force sensor that is nearest the user on the bed system, and receive a force data-stream corresponding to the identified force sensor, the biometric parameter of the user being determined based on the received force data-stream. In some implementations, detecting a presence of the user on the bed system can include: receiving the force data-stream from the force sensor and the second force data-stream from the second force sensor, identifying an amplitude for each of the force data-stream and the second force data-stream, determining whether the amplitude of the force data-stream or the amplitude of the second force data-stream exceeds a threshold amplitude value, and identifying a location of the user on the bed system as nearest the at least one leg based on the amplitude of the force data-stream exceeding the threshold amplitude value.
The controller can determine that the user and a partner are concurrently on the bed system based on applying a model to the force data-stream and the second force data-stream, the model having been trained with machine learning techniques to (i) isolate force data-streams of a partner-side of the bed system from force data-streams of a sleeper-side of the bed system and (ii) discard the force data-streams of the partner-side of the bed system. In some implementations, during a sleep session of the user, the controller can (i) receive the at least one force data-stream and (ii) determine the biometric parameter of the user at the predetermined time intervals. The controller can be configured to determine the biometric parameter of the user at the predetermined time intervals responsive to detection of bed presence of the user. The aggregate biometric parameter can be determined for a sleep session of the user. The predetermined time intervals can be 15-second windows. The predetermined time intervals can include a threshold amount of time after the user is detected to be awake. The predetermined time intervals may include a threshold amount of time after the user is detected to have left the bed system.
In some implementations, processing the at least one force data-stream can include: applying at least one filter to the force data-stream to remove noise from the force data-stream, identifying instances when the filtered force data-stream crosses a threshold value, and determining a respiration rate of the user based on the identified instances that the filtered force data-stream crosses the threshold value. The at least one filter can be a notch filter at 60 Hz. The at least one filter can be an 8th order Chebyshev low-pass filter at 40 Hz. The at least one filter can be a notch filter at 19 Hz. The at least one filter can be a 2nd order Chebyshev high-pass filter at 0.1 Hz. As another example, processing the at least one force data-stream can include applying a first filter, a second filter, a third filter, and a fourth filter to the force data-stream. The first, second, third, and fourth filters can be applied to the force data-stream in series. The first filter can be a notch filter, the second filter can be a low-pass filter, the third filter can be another notch filter, and the fourth filter can be a high-pass filter. Sometimes, the threshold value can be zero.
As another example, processing the at least one force data-stream can include applying a first filter, a second filter, and a third filter to the force data-stream. The first filter can be a notch filter, the second filter can be a low-pass filter, and the third filter can be a high-pass filter.
Sometimes, processing the at least one force data-stream can include: applying at least one filter to the force data-stream to remove noise from the force data-stream, and applying a model to the filtered force data-stream to determine a heartrate of the user. Processing the at least one force data-stream further can include resampling the filtered force data-stream. Resampling the filtered force data-stream can include aggregating the filtered force data-stream by 10 ms intervals. The at least one filter can be a notch filter at 50 Hz. Processing the at least one force data-stream can include aggregating the filtered force data-stream in predetermined time intervals. The predetermined time intervals can be 15-second windows of time. The model can be a deep neural network (DNN). The DNN can include 4 convolutional layers and 4 dense layers. Each of the convolutional layers can have a width of 30 milliseconds (ms). The model can be trained using machine-learning techniques to estimate heartrates of users based on ground truth heartrate measurements in a training dataset. The model can be trained to determine a heartrate of the user during the predetermined time intervals of a sleep session of the user. The predetermined time intervals can be 15-second windows of time during the sleep session. The model can also be trained to aggregate the heartrate for the predetermined time intervals to determine an average heartrate during the sleep session of the user. The predetermined time intervals can be 30-second windows of time during the sleep session. The at least one filter can be a notch filter at at least one of 60 Hz or 50 Hz. The at least one filter can be an 8th order Chebyshev low-pass filter at 40 Hz. The at least one filter can be a notch filter at 19 Hz. The at least one filter can be a 2nd order Chebyshev high-pass filter at 0.5 Hz. Applying at least one filter to the force data-stream can include applying a first filter, a second filter, a third filter, and a fourth filter to the force data-stream. The first filter can be a notch filter, the second filter can be a low-pass filter, the third filter can be another notch filter, and the fourth filter can be a high-pass filter.
As another example, processing the at least one force data-stream can include: applying at least one filter to the force data-stream to remove noise from the force data-stream and applying a model to the filtered force data-stream to determine a respiration rate of the user. The biometric parameter can be a heartrate variability (HRV) of the user. The biometric parameter can be a sleep stage of the user. Returning the aggregate biometric parameter of the user can include transmitting the aggregate biometric parameter to a computing device of the user for presentation, to the user, in a graphical user interface (GUI) display at the computing device.
One or more embodiments described herein can include a bed system including: a support element having at least one leg, at least one force sensor of the at least one leg, the force sensor being configured to sense a force applied to the bed system or the leg, and a controller that can be configured to: receive at least one force data-stream from the at least one force sensor, the at least one force data-stream representing a force sensed by the force sensor, determine a respiration rate of a user on the bed system based on processing the at least one force data-stream, in which processing the at least one force data-stream includes: identifying instances when the at least one force data-stream crosses a threshold value, the threshold value being zero, and determining the respiration rate of the user based on the identified instances that the filtered force data-stream crosses the threshold value, and return the respiration rate of the user.
The system can optionally include one or more of the following features. For example, processing the at least one force data-stream can include applying at least one filter to the force data-stream, the at least one filter being configured to remove noise from the force data-stream. The at least one filter can be a notch filter at at least one of 60 Hz or 50 Hz. The at least one filter can be an 8th order Chebyshev low-pass filter at 40 Hz. The at least one filter can be a notch filter at 19 Hz. The at least one filter can be a 2nd order Chebyshev high-pass filter at 0.1 Hz. In some implementations, processing the at least one force data-stream can include applying a first filter, a second filter, a third filter, and a fourth filter to the force data-stream. The first, second, third, and fourth filters can be applied to the force data-stream in series. The first filter can be a notch filter, the second filter can be a low-pass filter, the third filter can be another notch filter, and the fourth filter can be a high-pass filter. Sometimes, processing the at least one force data-stream can include applying a first filter, a second filter, and a third filter to the force data-stream. The first filter can be a notch filter, the second filter can be a low-pass filter, and the third filter can be a high-pass filter.
As another example, the respiration rate can be determined, by the controller, at predetermined time intervals during a sleep session of the user. The predetermined time intervals can be 15-second windows of time during the sleep session of the user. Processing the at least one force data-stream can include applying a machine learning trained model to the force data-stream, the model being a DNN. The at least one force sensor can be a load-cell.
One or more embodiments described herein may include a bed system including: a support element having at least one leg, at least one force sensor of the at least one leg, the force sensor being configured to sense a force applied to the bed system or the leg, and a controller that can be configured to: receive at least one force data-stream from the at least one force sensor, the at least one force data-stream representing a force sensed by the force sensor, determine a heartrate of a user on the bed system based on applying a model to the at least one force data-stream, the model being a neural network, and return the heartrate of the user.
The system can optionally include one or more of the following features. For example, the controller can also be configured to apply at least one filter to the force data-stream, the at least one filter being configured to remove noise from the force data-stream. The controller can also be configured to resample the filtered force data-stream. Resampling the filtered force data-stream can include aggregating the filtered force data-stream by 10 ms intervals. The controller can also aggregate the filtered force data-stream in predetermined time intervals and apply the model to the aggregated force data-stream at the predetermined time intervals to determine a heartrate of the user at each of the predetermined time intervals. The controller can aggregate the heartrates at the predetermined time intervals to determine an average heartrate for the user. The predetermined time intervals can be 15-second windows of time. The at least one filter can be a notch filter at at least one of 60 Hz or 50 Hz. The at least one filter can be an 8th order Chebyshev low-pass filter at 40 Hz. The at least one filter can be a notch filter at 19 HZ. The at least one filter can be a 2nd order Chebyshev high-pass filter at 0.5 Hz.
Sometimes, applying at least one filter to the force data-stream can include applying a first filter, a second filter, a third filter, and a fourth filter to the force data-stream. The first filter can be a notch filter, the second filter can be a low-pass filter, the third filter can be another notch filter, and the fourth filter can be a high-pass filter. Sometimes, the model can be a DNN. The DNN can include 4 convolutional layers and 4 dense layers. The model can be trained using machine learning techniques to estimate heartrates of users based on ground truth heartrate measurements in a training dataset. The at least one force sensor can be a load-cell.
One or more embodiments described herein can include a system for determining a heartrate of a user of a bed based on processing load-cell signals from a load-cell of the bed using a machine-learning trained model.
One or more embodiments described herein can include a system for determining a respiration rate of a user based on processing load-cell signals from a load-cell of a bed using a zero-cross detection process. The zero-cross detection process can include: plotting the load-cell signals on a graph, identifying at least one point where the plotted load-cell signals crosses a zero value of a y axis of the graph, and correlating the at least one point with a breath of the user.
One or more embodiments described herein can include a system for determining, based on processing load-cell signals from a load-cell of a bed using a model trained with machine-learning techniques, at least one of (i) a heartrate of a user of the bed or (ii) a respiration rate of the user.
Any of the embodiments described herein can include one or more of the following features. For example, any of the embodiments described herein can include a mattress supported by the support element. The at least one filter can also configured to remove outlier data generated by the at least one force stream. Processing the at least one force data-stream can include: plotting the at least one force data-stream on a graph, identifying at least one point where the plotted force data-stream crosses a zero value of a y axis of the graph, and correlating the at least one point with a breath of the user to determine a respiration rate of the user.
One or more embodiments described herein can include a bed system including: a support element having a head end and a foot end, at least a first leg positioned at or near the head end of the support element, at least a second leg positioned at or near the foot end of the support element, a first force sensor of the first leg that can be configured to sense force applied to the first leg, a second force sensor of the second leg that can be configured to sense force applied to the second leg, and a controller that can be configured to: receive a first force data-stream from the first force sensor and a second force data-stream from the second force sensor, the first and second force data-streams representing forces sensed by the respective first and second force sensors, select, for processing, the first force data-stream from the first force sensor based on a distance between the first force sensor and the head end of the support element, determine at least one biometric parameter of a user on the bed system based on processing the first force data-stream without use of the second force data-stream, and return the at least one biometric parameter of the user.
The system described herein can optionally include one or more of the following features. For example, the bed system can also include a third leg having a third force sensor at the head end of the support element and a fourth leg having a fourth force sensor at the foot end of the support element. Only the first leg and the third leg may have respective force sensors. The bed system can be a king-sized bed system having eight legs and eight corresponding force sensors. The bed system can have five legs and five corresponding force sensors. The bed system can have four legs and four corresponding force sensors. The at least one biometric parameter can include a heartrate and a respiration rate. Determining the heartrate can include applying a model to the first force data-stream, the model having been trained with machine learning techniques to estimate heartrate values based on force signals in force data-streams. Determining the respiration rate can include filtering the first force data-stream and applying a zero cross detection process to the filtered first force data-stream. The heartrate and the respiration rate can be determined in series. The heartrate and the respiration rate can be determined in parallel.
One or more embodiments described herein can include a bed system including: a support element having at least one leg, at least one force sensor of at least one leg, the force sensor being configured to sense a force applied to the bed system or the leg, and a controller that can be configured to: receive a force data-stream from the force sensor, the force data-stream representing a force sensed by the force sensor, determine a respiration rate of a user on the bed system based on filtering the force data-stream and applying a zero cross detection process to the filtered force data-stream, determine a heartrate of the user on the bed system based on applying a model to the force data-stream, and return the respiration rate and the heartrate of the user.
The system can optionally include one or more of the following features. For example, the respiration rate and the heartrate can be determined in series. The respiration rate and the heartrate can be determined in parallel. The respiration rate and the heartrate can be determined using the same force data-stream from the same force sensor of the bed system.
One or more embodiments described herein can include a method including: receiving, by a computing system, at least one force data-stream from at least one force sensor of at least one leg of a support element of a bed system, the at least one force data-stream representing a force sensed by the force sensor, determining, by the computing system, a biometric parameter of a user on the bed system at predetermined time intervals based on processing the at least one force data-stream, generating, by the computing system, an aggregate biometric parameter of the user based on aggregating the biometric parameters for the predetermined time intervals, and returning, by the computing system, the aggregate biometric parameter of the user. The method can optionally include one or more of the abovementioned features.
One or more embodiments described herein can include a method including: receiving, by a computing system, at least one force data-stream from at least one force sensor of at least one leg of a support element of a bed system, the at least one force data-stream representing a force sensed by the force sensor, determining, by the computing system, a respiration rate of a user on the bed system based on processing the at least one force data-stream, in which processing the at least one force data-stream can include: identifying, by the computing system, instances when the at least one force data-stream crosses a threshold value, the threshold value being zero, and determining, by the computing system, the respiration rate of the user based on the identified instances that the filtered force data-stream crosses the threshold value, and returning, by the computing system, the respiration rate of the user. The method can optionally include one or more of the abovementioned features.
One or more embodiments described herein can include a method including: receiving, by a computing system, at least one force data-stream from at least one force sensor of at least one leg of a support element of a bed system, the at least one force data-stream representing a force sensed by the force sensor, determining, by the computing system, a heartrate of a user on the bed system based on applying a model to the at least one force data-stream, the model being a neural network, and returning, by the computing system, the heartrate of the user. The method can optionally include one or more of the abovementioned features.
One or more embodiments described herein can include a method including: receiving, by a computing system, a first force data-stream from a first force sensor and a second force data-stream from a second force sensor, the first force sensor being attached to a first leg of a support element, the first leg being positioned at or near a head end of the support element, the second force sensor can be attached to a second leg of the support element, the second leg being positioned at or near a foot end of the support element, the first and second force data-streams represent forces sensed by the respective first and second force sensors, selecting, by the computing system and for processing, the first force data-stream from the first force sensor based on a distance between the first force sensor and the head end of the support element, determining, by the computing system, at least one biometric parameter of a user on the bed system based on processing the first force data-stream without use of the second force data-stream, and returning, by the computing system, the at least one biometric parameter of the user. The method can optionally include one or more of the abovementioned features.
One or more embodiments described herein can include a method including: receiving, by a computing system, a force data-stream from a force sensor of a leg of a support element of a bed system, the force data-stream representing a force sensed by the force sensor, determining, by the computing system, a respiration rate of a user on the bed system based on filtering the force data-stream and applying a zero cross detection process to the filtered force data-stream, determining, by the computing system, a heartrate of the user on the bed system based on applying a model to the force data-stream, and returning, by the computing system, the respiration rate and the heartrate of the user. The method can optionally include one or more of the abovementioned features.
The devices, system, and techniques described herein may provide one or more of the following advantages. For example, the disclosed technology provides accurate determination and estimation of real-time biometric information of a user by leveraging existing technology of a bed system. Load cells or other types of force sensors that already be mounted to the bed system can collect signals detected when the rests on the bed. The signals can then be processed by a computing system using filtering techniques, rules, and/or machine learning models to accurately determine one or more biometrics of the user during a sleep session. As a result, the biometric information of the user can be quickly and efficiently determined without requiring additional components, such as sensors and processing units, to be added to the bed system.
Similarly, the disclosed technology can determine the biometric information of the user using load-cell signals from a single load cell of the bed system. The disclosed technology can process load-cell signals collected nearest a head end of the bed system where signals indicative of breathing, heartrate, etc. may be most prevalent. Processing the signals closest to where the user breathes and their heart beats can result in improved accuracy in determining and estimating the biometric information of the user. As a result of collecting and processing load-cell signals from the single load cell (rather than collecting and processing signals from many load cells of the bed system), the disclosed technology can avoid clogging network bandwidth, increase processing power, and use fewer compute resources. After all, collecting and processing the signals from a single load cell may not require transmitting large amounts of signals (e.g., data streams) across networks. Such efficient and lightweight processing can also result in improved accuracy in determining the biometric information of the user.
The disclosed technology also leverages machine learning models to determine certain types of biometric information that may be less apparent from load-cell signals, such as heartrate. Therefore, the same load-cell signals can be processed using different techniques in order to accurate determine different types of biometric information of the user. In particular, the disclosed technology can utilize neural networks, such as deep neural networks (DNNs) to accurately determine the heartrate of the user from the load-cell signals. DNNs may not require as much retraining as other types of machine learning techniques, thereby reducing size, space, and bandwidth needed during model training and retraining. DNNs are less heavy and less computationally complex to train compared to other techniques. Consequently, compute resources can be saved and efficiently used for performing real-time determinations of the user biometric information. The DNNs described herein may be trained with data collected by existing devices as ground truth measurements. The DNNs can also be run in parallel with other processes, such as rules that are applied to filter the load-cell signals and determine the user's respiration rate. Moreover, DNNs have improved accuracy compared over other types of models. Each next layer of a DNN can, for example, capture more complex dependency on prior layers whereas techniques implementing one layer frameworks may be too shallow to capture more complex wave forms than simple sequences of interchanging observations. The neural networks described herein can extract useful information from the load-cell signals, even when a signal to noise ratio is very low for those signals. Furthermore, since DNNs are not time-dependent models, one decision of the model may not depend on prior decisions of the model. As a result, additional memory elements that may otherwise be needed for recurrent neural networks are not required when deploying the DNNs described herein. Advantageously, the disclosed technology can also implement other machine learning techniques if a better signal quality is received and/or available hardware can efficiently deploy simplified techniques, including but not limited to decision trees. For example, in some implementations, time-dependent DNNs may be utilized, especially in scenarios in which historical information can be used to increase accuracy of model output(s).
As another example, the disclosed technology implements a windowed approach to determine heartrate in each window in real-time. The heartrates can then be aggregated to accurately determine an average heartrate of the user during an entire sleep session. Since the disclosed technology leverages existing load cells of the bed system, the disclosed technology also can account for low signals and noise that may be detected in real-time by the load cells at each window of time. The disclosed technology can implement techniques that make it more robust to signal quality, artifacts, changes with noise, and low signal-to-noise ratio (SNR) during real-time implementation. As a result, the disclosed technology provides accurate determinations and estimations of the user's biometric information in real-time, regardless of environmental or ambient conditions that otherwise may disrupt a quality of the signals collected by the load cell(s) of the bed system.
The disclosed technology can also provide for determining various characteristics of the user's biometric information. For example, the disclosed technology can implement a bank of filters and processing rules to accurately identify maxes (e.g., peaks) in the load-cell signals. The disclosed technology can then identify inspirations (breaths in) and expirations (breaths out) of the user's breath as inclines to the identified maxes and declines from the identified maxes, respectively. Therefore, the disclosed technology can provide more robust analysis of breathing patterns of the user. Such information can be transmitted to the user, healthcare providers, or other relevant users to determine, identify, and/or address user-specific sleep quality and/or health conditions. The disclosed technology can provide for real-time, non-invasive health monitoring of the user from the comfort of the user's home, thereby reducing cost and time of healthcare services and providing real-time or near real-time diagnosis of health conditions for purposes of preventative care.
As another example, the disclosed technology provides for observing cross-talk and interference of load-cell signals in the bed system when two users are resting on the bed system at the same time. For example, a model can be trained to isolate signals corresponding to a sleeper from signals corresponding to a partner and then accurately determine biometric information specific to each user. The model can also be trained to disassociate the signals of the sleeper from the signals of the partner, then discard the signals of the partner and process the remaining signals of the sleeper to accurately determine the biometric information of the sleeper.
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.
Like reference symbols in the various drawings indicate like elements.
This document generally describes systems, methods, and techniques for determining biometric information about a user of a bed system while the user is detected in the bed system and/or during a sleep session of the user. The bed system can include at least one load cell (e.g., force sensor) that can collect load-cell signals when the user rests on the bed system. The load-cell signals can be transmitted to a computing system (e.g., a controller of the bed system, a remote computing system) for processing. For example, the load-cell signals can be filtered using a filter bank and one or more rules, such as a zero-cross detection process, to determine the user's respiration rate at predetermined intervals during the sleep session and/or for an entire duration of the sleep session. Additionally or alternatively, the load-cell signals can be processed with one or more machine learning-trained models to determine heartrate or other biometric information. Any combination of rules and/or models can be employed by the disclosed technology to accurately determine the biometric information of the user during the sleep session.
Example Airbed Hardware
As illustrated in
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.
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
The air bed system 100 in
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 active 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
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). Thee control circuitry 334 can also be included in the control box 124 of
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
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
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 comfortability, 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.
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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).
In
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.
In
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.
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.
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
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.
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
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.
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
Sometimes, two 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.
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.
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.
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.
The pressure sensor manager 1310 can include, or reference, data related to the configuration and operation of pressure sensors in beds. This data can include an identifier of the types of sensors in a particular bed, their settings and calibration data, etc. The pressure based sleep data 1312 can use raw pressure sensor data 1314 to calculate sleep metrics tied to pressure sensor data. For example, user presence, movements, weight change, heartrate, and breathing rate can be determined from raw pressure 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-pressure 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 pressure sensor data 1314 and non-pressure sleep data 1316 (e.g., raw temperature data). 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. 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.
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.
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.
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.
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 comfortability. 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.
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.
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 chip set 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.
The sensors 1908A-N can be in communication with a computer system 1902 via network(s) 1906 (e.g., wired and/or wireless communication). The computer system 1902 can be a remote computing system, such as a cloud-based service. Sometimes, the computer system 1902 can be a controller of the bed system 1900. The computer system 1902 may also be a user device, such as user device 1910. The user 1904's user device 1910 can be any type of mobile computing device, including but not limited to a cellphone, smartphone, mobile phone, laptop, tablet, and/or wearable device. The user device 1910 may also be in communication with the computer system 1902 via the network(s) 1906. The user device 1910 can receive and present information about the user 1904's current and/or prior sleep sessions in a mobile application. The user device 1910 can also present other information in the mobile application about the user 1904's health, as determined, for example, by the computer system 1902 using the techniques described herein.
As shown in
In some implementations, the sensors 1908A-N can transmit only some or less than all of the collected sensor data. For example, only the sensor data collected by the sensors 1908A-N or a single sensor nearest a head end of the bed system 1900 can be transmitted to the computer system 1902. As described herein, this can advantageously save compute resources and processing power while avoiding clogging network bandwidth since less data may be transmitted over the network 1906 and processed by the computer system 1902. Moreover, the sensor data collected near the head end of the bed system 1900 may provide improved accuracy in detecting biometric information about the user 1904 since the user's breaths and heartrates may be sensed more easily closer to their head rather than their feet. In some implementations, the computer system 1902 can receive the sensor data collected from multiple sensors 1908A-N in order to increase accuracy of biometric determinations made by the computer system 1902.
Optionally, the computer system 1902 can detect bed presence of the user 1904 based on the sensor data (block B, 1914). This determination can be made based on processing the sensor data and/or other sensor data received from the sensors 1908A-N of the bed system 1900 (e.g., force/load-cell data, pressure data, temperature data, or any combination thereof). The computer system 1902 can perform any one or more presence detection techniques described herein to determine whether the bed system 1900 is occupied and if so, on what side of the bed system 1900 the user 1904 is sleeping/resting. The computer system 1902 can then identify one or more sensors 1908A-N nearest a head of the user 1904 (or the user 1904 in general, such as sensors positioned on a right side of the bed where the user is resting/detected). The computer system 1902 can poll the identified sensors for their collected sensor data, which can be processed by the computer system 1902 to determine (e.g., estimate) the user 1904's biometric information. Sometimes, the computer system 1902 can receive the sensor data from all of the sensors 1908A-N, and then only select a portion of the sensor data that corresponds to the side of the bed system 1900 where the user was detected. In some implementations, the computer system 1902 can determine which sensor data to keep for further processing based on pre-processing the sensor data. For example, the computer system 1902 can analyze prominence of breathing signals based on their amplitudes in the sensor data to determine which sensor data to keep for further processing and which sensor data to discard (and/or to determine/detect user bed presence).
The computer system 1902 can filter the sensor data in block C (1916). The computer system 1902 can apply a bank of filters (or at least one filter) to the sensor data based on a type of biometric value to be determined. For example, a first set of filters can be applied to the sensor data to determine a breathing or respiration rate of the user 1904 while a second set of filters can be applied to the same sensor data to determine a heartrate of the user 1904. The filters applied to the sensor data can vary depending on the type of biometric value to be determined because different types of biometrics may be represented by different signal strengths/frequencies in the sensor data. Refer to
The computer system 1902 can process the filtered data to determine the respiration rate of the user 1904 (block D-1, 1918). Additionally or alternatively, the computer system 1902 can process the filtered data to determine the heartrate of the user 1904 (block D-2, 1920). Blocks D-1 and D-2 can be performed in parallel. Therefore, the respiration rate and heartrate of the user 1904 can be determined simultaneously, using the same sensor data but different filtering techniques and/or processing techniques. In some implementations, blocks D-1 and D-2 can be performed at different times and/or in series. The blocks D-1 and/or D-2 can also be performed to determine respiration rate and/or heartrate at predetermined time intervals throughout the sleep session of the user 1904. The computer system 1902 can also determine an average respiration rate and/or average heartrate based on aggregating (e.g., averaging) the respiration rates and/or the heartrates, respectively, that were determined at the predetermined time intervals. Accordingly, the computer system 1902 can perform the blocks D-1 and/or D-2 in real-time, as the sensor data is collected. The computer system 1902 can additionally or alternatively perform the blocks D-1 and/or D-2 at an end of the user 1904's sleep session (e.g., when the user 1904 is detected as waking up or leaving the bed system 1900).
As described herein, the respiration rate can be determined using simple logic and rulesets, especially since signals indicative of breathing can be more readily apparent in the sensor data (block D-1, 1918). Advantageously, such techniques can be lightweight and utilize minimal compute resources and processing power. As a result, compute resources and processing power of the computer system 1902 can be used for other heavier-weight processes. The computer system 1902 can determine the respiration rate based on applying a bank of predetermined filters to the sensor data and then a zero cross detection process. Refer to
Since cardiac signals (e.g., heartbeat) may be less readily apparent in the sensor data (e.g., having a lower amplitude), additional processing techniques may be used to determine heartrate of the user 1904 (block D-2, 1920). The computer system 1902 can determine the heartrate based on applying a different bank of predetermined filters to the sensor data. The computer system 1902 may also apply one or more machine learning models to the filtered sensor data to identify and analyze signals indicative of heartbeat and, accordingly, determine the user 1904's heartrate. Refer to
The computer system can return the determined biometric information (e.g., the heartrate and/or the respiration rate) of the user 1904 in block E (1922). The biometric information can, for example, be transmitted to the user device 1910 and outputted in a GUI display of a mobile application launched at the user device 1910. The biometric information can be viewed by the user 1904 at their user device 1910 when they wake up from their current sleep session, a threshold amount of time after waking up from the sleep session (e.g., 15 minutes later, or whenever the user 1904 first opens the mobile application at the user device 1910 after waking up), and/or whenever the user 1904 desires to view the biometric information at their user device 1910. The user 1904 can view the biometric information to learn about their sleep quality, sleep health, overall health, and/or medical/health conditions. The user 1904 can also view the biometric information to make changes in their habits and/or routines to improve their biometrics, health, and/or sleep quality.
The biometric information can also be transmitted to computing devices of other relevant stakeholders, such as medical providers, doctors, caretakers, parents, etc. The biometric information can be used by the other relevant stakeholders to determine health conditions of the user 1904, monitor the user 1904's sleep, and/or make diagnoses/health/sleep recommendations for the user 1904.
The biometric information can be transmitted to a data repository for storage and future retrieval. The biometric information can also be transmitted to another computing system for additional processing. In some implementations, the biometric information can be used, by the computer system 1902 or another computing system, to determine one or more sleep or health quality metrics of the user 1904. For example, the respiration rate and/or the heartrate can be used as an input factor in generating a sleep quality score for the user 1904's sleep session. The respiration rate and/or the heartrate can also be used as an input factor in determining whether the user 1904 is developing any health or medical conditions, such as an illness, fever, cold, etc. The biometric information may also be used in one or more other processes as described throughout this disclosure in order to determine additional information about the user 1904, their sleep quality, and/or their overall health.
As described in reference to
In some implementations, quantity and placement of the sensors 1908A-N at the bed system 1900 may also vary depending on use-case. If the bed system 1900 is configured to determine and sense biometrics of the user(s), then the sensors 1908A-N can be attached to legs near the head end of the bed system 1900. On the other hand, if the bed system 1900 is being used to determine other information, such as bed presence, then the sensors 1908A-N may also be attached to other parts of the bed system 1908A-N, such as all the legs of the bed system 1900 or at least one leg near the head end and at least one leg near a foot end of the bed system 1900. In some implementations, the disclosed techniques can be implemented with any type of bed system 1900 that has already been manufactured and/or retrofitted with the sensors 1908A-N.
In the example of
Still referring to
The collected sensor data can be transmitted to the computer system 1902 and used by the computer system 1902 to detect sleeper and/or partner bed presence in block B (1952). For example, using some of the techniques described herein, the computer system 1902 can detect bed presence of the sleeper, user 1904B, and the partner, user 1904A, using at least the pressure data that was collected by the sensors 1908A-N. The computer system 1902 can also use any other collected sensor data and/or combination thereof to detect bed presence of the users 1904A and 1904B.
Once the computer system 1902 determines the bed presence of the sleeper, user 1904B, and the bed presence of the sleeper, user 1904A, the computer system 1902 can select the sensor data corresponding to a location or side of the bed system 1902 where the sleeper, user 1904B, is resting (block C, 1954). Here, the computer system 1902 can determine that the sleeper, user 1904B, is located on the left side of the bed system 1902 and their head is nearest the sensor 1908B. Accordingly, the computer system 1902 can select the sensor data (e.g., load-cell signals) collected by the sensor 1908B for further processing to determine the sleeper's biometric information. In some implementations, in block C, the computer system 1902 can discard the sensor data collected by the other sensors 1908A, 1908C, and/or 1908N. Sometimes, the computer system 1902 can process the sensor data collected by the sensor 1908B to determine the biometric information of the sleeper, user 1904B, and then process the sensor data collected by the sensor 1908A to determine the biometric information of the partner, user 1904A.
The computer system 1902 can process the selected sensor data to determine biometric information of the sleeper, user 1904B, in block D (1956). The computer system 1902 can process the sensor data as described herein, in reference to
The computer system 1902 can return the determined biometric information for the sleeper, user 1904B (and optionally for the partner, user 1904A) in block E (1958). Refer to
In the process 2000, the computer system can receive raw load-cell data from load cells of the bed system (block 2002). Refer to
The computer system can apply one or more predetermined filters to the raw load-cell data in block 2004. The at least one filter can be applied to the data to remove any noise. The computer system can apply a bank of filters. The filters can be applied in series. In some implementations, the filters can be applied in parallel.
In the example of
Next, the computer system can apply an 8th order Chebyshev low-pass filter at 40 Hz. Here, the computer system can also apply any other type of analog or digital filter or low-pass filter that can be used to minimize error between idealized and actual filtered characteristics over a range of the filter and thus separate one band of frequencies from another brand of frequencies. For example, such a filter can have passband ripple or stopband ripple. This filter can be applied at such a predetermined frequency in order to remove components that may be too high in the spectrum and therefore not useful to determining respiration rate. This filter can also allow for resampling of the data at approximately 100 Hz, which can be beneficially used for heartrate estimations and determinations also performed by the computer system (e.g., refer to
Next, the computer system can apply a second notch filter at 19 Hz. The second notch filter may not be applied in all implementations of the disclosed techniques. For example, the second notch filter may be applied in scenarios in which additional noise or particular types of signals and/or quality issues are detected in the data. As another example, the second notch filter may be applied but at a different predetermined frequency, where the predetermined frequency can vary based on the data collected and what additional noise, signal, and/or quality issues are detected in that collected data. Moreover, the second notch filter may not be needed if hardware and/or software configurations used by the disclosed techniques also changes.
Finally, the computer system can apply a 2nd order Chebyshev high pass filter at 0.1 Hz. The computer system can also apply any other type of high pass filter that can be used to remove any drift of signal and/or noise in the data. In some implementations, a frequency for the filter that is higher than 0.1 Hz may interfere with accurate detection of a respiratory cycle. Respiratory signals manifest in the filtered data as an oscillatory component of relative high amplitude from which a respiratory cycle of the user and consequently, a respiratory rate can be extracted.
Once the computer system filters the data in block 2004, the computer system can perform a zero-cross detection process (block 2006). The zero-cross detection process can be performed, for example, instead of a Fast Fourier Transform (FFT) because signals indicative of a respiratory cycle may not necessarily be regular—the user's breathing patterns can change throughout their sleep session. A signal from the filtered data can be centered around a value of 0 along a y axis (e.g., signal amplitude) and processed in a time domain, especially since the signal slowly moves up and down as the user breathes in and breathes out. The zero-cross detection process can be used to determine when the signal moves up above 0 along the y axis and then returns below 0. In other words, the computer system can detect positive and negative crossings of the 0 threshold to characterize values of the signal and a derivative of the signal. For example, if the value (e.g., amplitude) of the signal changes from a negative to a positive and the derivative of a few samples of the signal (e.g., 10 samples) has a positive slope, then the computer system can detect a positive zero-crossing. If the value of the signal changes from a positive to a negative and the derivative of the few samples of the signal has a negative slope, then the computer system can detect a negative zero-crossing. Each crossing can indicate a breath of the user.
Accordingly, the zero-cross detection process can be performed in order to detect a respiratory cycle of the user in block 2008. The computer system can identify each crossing of the zero threshold throughout the sleep session of the user or for predetermined time intervals to detect the breathing rate of the user. For example, in a minute-long interval, the computer system can detect/identify 12 crossings of the zero threshold, which can indicate that the user takes 12 breaths per minute. The computer system can also aggregate or average the breathing rates detected for each predetermined time interval to determine an average breathing rate or respiratory cycle for the user during the entire sleep session.
In some implementations, the computer system can also identify maxes or peaks in the signal. Once the maxes are identified, the computer system can categorize an incline in the signal up to a peak as an inspiration (breath in) and a decline in the signal down from the peak as an expiration of breath (e.g., breath out). As a result, the computer system can generate biometric information about particular inspiration (breathing in) and expiration (breathing out) patterns of the user throughout their sleep session (or for predetermined time intervals).
Moreover, although the zero-cross detection process in block 2006 is described using a vertical threshold value of zero, another vertical threshold value can be used for the cross detection process. That vertical threshold value can vary depending on a particular use case/implementation. Therefore, the signal can have an offset, which can determine a new level L around which to detect L-crossings. As an illustrative example, the vertical threshold value can include, but is not limited to −1, 1, 2, 2.5, 3, 3.5, 4, 4.5, 5, etc.
Graph 2010 indicates determined respiration rate for the user using the process 2000 described above. In the illustrative example of the graph 2010, load-cell signals can be originally acquired at a sampling frequency of 250 Hz (block 2002). These signals can be notch filtered at 60 Hz and low-pass filtered at 40 Hz (block 2004). The resulting filtered signals can then be subsampled at 100 Hz, and finally high-pass filtered at 0.1 Hz, for breathing or respiration rate analysis, or 0.5 Hz, for heartrate analysis (block 2004). Once the zero-crossing detection process is applied to the filtered signals (block 2006), the computer system detects the respiratory cycle of the user (block 2008), as shown by the peaks in the graph 2010.
The processes 2100 and 2110 can be performed by a computing system, such as the computing system 1902 in
As shown in the process 2100 in
In block 2104, the computer system can apply one or more filters to the received data. Refer to block 2004 in the process 2000 in
The computer system can resample the filtered signal in block 2106. An illustrative resampled signal is shown in graph 2107 in
Once resampled, the computer system can collect sequences of the signals, which can be fed into a machine learning model to determine and detect the user's heartrate (block 2108). A sequence can be a window of time that includes the signal values (e.g., post-processed, filtered, and/or resampled signal values). For example, the sequence can include the signals that have been resampled at 100 Hz (10 ms sampling period). In some implementations, the sequence can contain 15 seconds of the signal values. In some implementations, the computer system can build a ring buffer, which can update the current window (e.g., sequence) every second or other predetermined time interval(s) to remove a first second of data and append a new second of data from the signal values. As an illustrative example, if x1, x2, . . . , x1500 is data of the buffer at a time of 1 second, then at a time of 2 seconds, the data of the buffer would be x101, x102, . . . ,x1600. Refer to the process 2110 in
Referring to the process 2110 in
The pre-processed signals 2112 (e.g., the collected sequences of the signals) can be segmented, by the computer system, into windows 2114A-N. The windows 2114A-N can be predetermined time intervals. For example, the windows 2114A-N can each be a 15-second long window. The 15-second long windows can provide optimized and accurate results of heartrate detection, although other window lengths can also be used with the disclosed techniques. A shift 2116 of 1 second can be applied to the signals 2112 such that a 14-second overlap occurs between adjacent windows 2114A-N. The sequences of the signals (e.g., samples) in a particular window can then be fed to a model 2118 for processing. The model 2118 can be trained to determine a heartrate 2120 of the user for the particular window. Then, the computer system can determine an average heartrate of the user for an entire sleep session or other predetermined period of time based on aggregating the heartrates 2120 determined for each window of time 2114A-N. After all, aggregation of several windowed heartrates can allow for accurate detection of an average heartrate of the user over some predetermined period of time (e.g., an epoch, such as 30 second windows, multiple epochs, a sleep session).
The model 2118 can be a DNN composed of 4 convolution layers and one dense layer. In some implementations, the model 2118 can have 3 dense layers. Sometimes, the model 2118 can have 4 dense layers. A model with 4 convolution layers and 4 dense layers can provide highest accuracy in detecting heartrate in comparison to a model with fewer or additional convolution layers and dense layers, in some implementations. The model 2118 can have additional or fewer convolution layers and/or dense layers. Each convolution layer can have predetermined filter widths and number of filters, based on training of the model 2118. For example, with 4 convolution layers, each layer can have a width of 30 ms, so that the layer processes three neighboring samples. In some implementations, the input signal can have 10 ms periodicity, resulting in the width of each layer being a multiple of 10. Moreover, each next layer in the model 2118 can advantageously capture a more complex dependency or more complex wave forms, thereby providing higher accuracy in detecting heartrate of the user compared to models with fewer layers. However, it will be appreciated that in some situations fewer layers may be more advantageous, such as in resource-constrained computing environments.
The model 2118 can be trained with data collected from a threshold quantity of prior sleep sessions of the user, another user, and/or a group of users (e.g., the group can include the user, the group can include users sharing one or more demographics with the user, the group can include users who are not similar to the user). For example, the model 2118 can be trained with 1,500 samples per window 2114A-N and with load-cell signals collected from 24 sleep sessions. The model 2118 can be trained with any other desired quantity of samples per window and/or quantity of sleep sessions in order to ensure and/or improve accuracy of the model 2118 during runtime use.
As described herein, a DNN can provide numerous advantages over other types of machine learning techniques, depending on implementation. The DNN can, for example, be less heavy and less computationally complex to train than other models, such as convolution neural networks (CNNs). Multiple DNNs can be run in parallel so that, for example, the heartrate for multiple windows 2114A-N can be determined simultaneously, at a same time. How many DNNs can be run at a same time can depend on available CPU, kernels, and other available compute resources of the computer system. Similarly, since a DNN does not have recurrent layers, the windows 2114A-N do not need to be processed one-by-one, thereby making the DNN lightweight, less computationally complex to train and run, and producing increasingly accurate heartrate determinations. Decisions of the DNN do not depend on prior model decisions, which results in the model requiring less memory elements than other machine learning techniques, such as recurrent neural networks. A neural network such as a DNN may also be used because the SNR is very low for heartrate detection, as described above, and a DNN can advantageously extract useful information from the load-cell signals to accurately detect low-signal heartrate.
In some implementations, however, if improved signal quality and/or hardware is implemented, other simpler models, such as decision trees, can be used to accurately detect heartrate and other biometric information of the user. As another illustrative example, the model 2118 may be a convolution neural network (CNN) in some scenarios. As such, the model 2118 can receive, as inputs, signals from more than one load cell sensor (such as 2 or more load cell sensors). The model 2118's CNN layers can then process the signals from each load cell sensor separately (e.g., the CNN layers' weights can be trained by each channel separately). The CNN layers may also process the signals from each load cell sensor together.
In some implementations, the model 2118, or a similar model, can be used to also determine the respiration rate of the user. In such scenarios, the windows 2114A-N can be adjusted to longer intervals of time to accurately determine the respiration rate. For example, the windows 2114A-N can be adjusted to 1-minute, 2-minute, 3-minute, 4-minute, 5-minute, etc. windows. Moreover, the model 2118 can be used to detect one or more other biometrics of the user. For example, the model 2118, or a similar model, can be used to determine heartrate variability (HRV) of the user. Since a heartrate value can be detected/collected for every second, the model 2118 can be trained to determine HRV over different windows 2114A-N, such as 5-minute windows. The model 2118 can additionally or alternatively be used to determine sleep stages of the user.
Referring to
Referring to the process 2200, the computer system can receive sensor data from one or more force sensors of a bed system in block 2202. Refer to block A (1912) in
In some implementations, the support element further can include a second leg having a second force sensor. The computer system may also receive a second force data-stream from the second force sensor and determine a biometric parameter of a user at predetermined time intervals based on processing the at least one force data-stream and the second force data-stream.
The computer system can also detect bed presence of the user based on the sensor data (block 2204). Refer to block B (1914) in
Optionally, the computer system can also select a portion of the sensor data corresponding to a location of the detected user bed presence (block 2206). Refer to block C (1954) in
As another example, the first leg can be at or near a head end of the support element and the second leg can be at or near a foot end of the support element. The at least one force sensor can be configured to the first leg to sense force applied to the first leg and the second force sensor can be configured to the second leg to sense force applied to the second leg. The computer system can then receive a first force data-stream from the at least one force sensor and a second force data-stream from the second force sensor. The first and second force data-streams represent forces sensed by the respective at least one and second force sensors. The computer system can then select, for processing, the first force data-stream from the at least one force sensor based on a distance between the at least one force sensor and the head end of the support element. The computer system can process the first force data-stream without use of the second force data-stream in order to determine at least one biometric parameter of the user on the bed system. Moreover, the bed system can have a third leg having a third force sensor at the head end of the support element and a fourth leg having a fourth force sensor at the foot end of the support element. Sometimes, only the first leg and the third leg may have respective force sensors, as they are nearest the head end of the support element. In some implementations, the bed system can be a king-sized bed system having eight legs and eight corresponding force sensors. Sometimes, the bed system can have five legs and five corresponding force sensors. The bed system may also have four legs and four corresponding force sensors.
In some implementations, the computer system can be pre-programmed (such as when setting up the bed system) to know which sensor data to select and use based on location of the force sensors on the bed system. For example, the computer system can be programmed to know that a first force sensor on a first leg is located at a head end of the bed system. During runtime application of the disclosed techniques, the computer system can select sensor data generated by the first force sensor, regardless of whether other force sensors on other legs of the bed system collect sensor data as well. In some implementations, if a second leg is also located at the head end of the bed system and the second leg has a second force sensor, then the computer system can be programmed to select and use sensor data generated by both the first and second force sensors.
Moreover, in some implementations, the computer system can determine which sensor data to use based on analysis of a respiratory cycle of a user of the bed system. The computer system can detect a location of the user's head by determining a largest amplitude of a respiratory cycle, as detected in sensor data received from the force sensors of the bed system. The respiratory cycle data can be used as a reference here because respiratory patterns can be easier to detect from signals than heartrate patterns. This implementation may also be beneficial in scenarios in which the user changes their orientation during a particular sleep session.
In some implementations, the computer system can perform blocks 2204 and 2206 to determine whether the user and a partner are concurrently on the bed system. To do so, the computer system can apply a model to the force data-stream and the second force data-stream, the model having been trained with machine learning techniques to (i) isolate force data-streams of a partner-side of the bed system from force data-streams of a sleeper-side of the bed system and (ii) discard the force data-streams of the partner-side of the bed system.
The computer system can filter the sensor data to reduce noise in signals of the sensor data (block 2208). Refer to block 2004 in the process 2000 of
The at least one filter can be a notch filter at 60 Hz. The at least one filter can be an 8th order Chebyshev low-pass filter at 40 Hz. The at least one filter can optionally be a notch filter at 19 Hz. The at least one filter can also be a 2nd order Chebyshev high-pass filter at 0.1 Hz. Sometimes, processing the at least one force data-stream can include applying a first filter, a second filter, a third filter, and a fourth filter to the force data-stream. The first, second, third, and fourth filters can be applied to the force data-stream in series. The first filter can be a notch filter, the second filter can be a low-pass filter, the third filter can be another notch filter, and the fourth filter can be a high-pass filter, as described herein. In some implementations, processing the at least one force data-stream can include applying a first filter, a second filter, and a third filter to the force data-stream. The first filter can be a notch filter, the second filter can be a low-pass filter, and the third filter can be a high-pass filter.
The computer system can also perform a zero-cross detection process on the filtered data in block 2210. Refer to block 2006 in the process 2000 of
In block 2212, the computer system can determine user respiration rate values for predetermined time intervals based on the zero-cross detection process. Processing the at least one force data-stream can include determining a respiration rate of the user based on the identified instances that the filtered force data-stream crosses the threshold value. Refer to block 2008 in the process 2000 of
Optionally, the computer system can aggregate the respiration rate values to determine an average respiration rate of the user for a sleep session (block 2214). Refer to block 2008 in the process 2000 of
The computer system can then return the respiration rate value(s) of the user in block 2216. Refer to block E (1922) in
Although the process 2200 is described in reference to determining the respiration rate of the user, the process 2200 can also be performed to determine other biometric parameters of the user, including but not limited to a heartrate variability (HRV) of the user and a sleep stage of the user.
Referring to the process 2300, the computer system can receive sensor data from at least one force sensor (e.g., load cell) of a bed system (block 2302). Refer to block 2202 in the process 2200 of
The computer system can optionally detect bed presence of the user of the bed system based on the sensor data in block 2304. Refer to block 2204 in the process 2200 of
Optionally, the computer system can select a portion of the sensor data that corresponds to a location of the detected user bed presence (block 2306). Refer to block 2206 in the process 2200 of
The computer system can then pre-process the sensor data in block 2308. Pre-processing the sensor data can include applying one or more filters to the sensor data. Refer to block 2208 in the process 2200 of
Processing the at least one force data-stream further can include resampling the filtered force data-stream. Resampling the filtered force data-stream can include aggregating the filtered force data-stream by 10 ms intervals. The force data-stream can also be aggregated using one or more other intervals, including but not limited to 2 ms intervals, 5 ms intervals, 15 ms intervals, 20 ms intervals, 25 ms intervals, 30 ms intervals, etc. Resampling the filtered force data-stream can also include aggregating the filtered force data-stream at 100 Hz. The filtered fore data-stream can also be aggregated using one or more other frequencies, such as a frequency value within a range from 50 Hz to 100 Hz.
In block 2310, the computer system can apply a model to the pre-processed sensor data to determine a heartrate value of the user at each predetermined time interval. For example, the computer system can perform steps including applying a model to the filtered force data-stream to determine a biometric parameter, such as the heartrate, of the user. Refer to
Sometimes, the computer system can aggregate the filtered force data-stream in predetermined time intervals and then apply the model to the aggregated force data-stream at the predetermined time intervals to determine the heartrate of the user at each of the predetermined time intervals. The computer system can also aggregate the heartrates at the predetermined time intervals to determine an average heartrate for the user.
As described herein, the model can be trained using machine-learning techniques to estimate heartrates of users based on ground truth heartrate measurements in a training dataset. The training dataset can include pre-processed and/or filtered sensor data collected from bed systems of one or more users. The model can also be trained to determine a heartrate of the user during the predetermined time intervals of a sleep session of the user. The predetermined time intervals can be 15-second windows of time during the sleep session, as described throughout this disclosure.
Optionally, the computer system can aggregate the heartrate values for the predetermined time intervals to determine an average heartrate of the user for a sleep session or other predetermined period of time (block 2312). Refer to block 2214 in the process 2200 of
The computer system can then return the heartrate value(s) of the user in block 2314. Refer to block 2216 in the process 2200 of
The process 2400 can be performed by a computing system, such as the computing system 1902 in
Referring to the process 2400 in
The computer system can filter the sensor data in block 2404. The computer system can filter the sensor data as described in reference to block 2004 in FIG. and block 2104 in
In block 2406, the computer system can determine a respiration rate of the user. For example, the computer system can apply a zero-cross detection process to the filtered data to determine a respiration rate per predetermined time interval during a sleep session of the user (block 2408). The computer system can also aggregate the determined respiration rates to determine an average respiration rate of the user for the entire sleep session (block 2410). The computer system can then proceed to block 2418, described further below. Refer to the process 2000 in
The computer system can also determine a heartrate of the user in block 2412. For example, the computer system can apply a model to the filtered data to determine a heartrate of the user per predetermined time interval during the sleep session of the user (block 2414). The computer system can also aggregate the determined heartrates to determine an average heartrate of the user for the entire sleep session (block 2416). Then, the computer system can proceed to block 2418. Refer to the process 2100 in
In some implementations, blocks 2406-2416 can be performed simultaneously/at the same time. In other words, the computer system can process, at the same time, the same filtered sensor data to determine the respiration rate of the user, the heartrate of the user, and optionally one or more other biometric parameters of the user. As a result, the computer system can determine more than one biometric parameter in real-time or near real-time to provide quick and accurate information to the user about their health and sleep. Moreover, the biometric parameters can then be used by the computer system and optionally other computing systems to quickly and accurately determine additional information about the user, their sleep quality, and/or their overall health.
The blocks 2406-2416 can be performed in parallel due in part to blocks 2406-2410 requiring minimal processing power and compute resources. After all, blocks 2406-2410 can be performed using lightweight, simple logic and rules. Similarly, at least block 2414 can be performed using a DNN model, which is less computationally complex and lighter weight than other machine learning techniques and algorithms that may be employed to accurately determine metrics like heartrate. Because block 2414 can be performed with lightweight machine learning techniques, available processing power and compute resources of the computer system can be utilized to determine multiple biometric parameters of the user at the same or similar time.
In some implementations, the respiration rate and the heartrate can be determined in series. Moreover, in some implementations, the computer system can determine, based on processing load-cell signals (e.g., the sensor data) from a load-cell (e.g., at least one force sensor) of the bed system and using a model trained with machine learning techniques, at least one of the heartrate of the user of the bed system or the respiration rate of the user.
Still referring to the process 2400, in block 2418, the computer system can optionally determine other biometric parameters of the user based on the filtered data. For example, the computer system can apply simple logic, rulesets, and/or machine learning models to the filtered data to determine heartrate variability (HRV) of the user. The computer system can additionally or alternatively apply simple logic, rulesets, and/or machine learning models to the filtered data to determine sleep stages of the user. One or more other biometric parameters may also be determined using the filtered data. In some implementations, the other biometric parameters can be determined using the sensor data received in block 2302. Therefore, the computer system can pre-process the sensor data using different filters and/or processing techniques before or as part of determining the other biometric parameters.
The computer system can then return the determined biometric parameters of the user in block 2420. Refer to at least block E (1922) in
This application claims the benefit of U.S. Provisional Application Ser. No. 63/401,870, filed Aug. 29, 2022. The disclosure of the prior application is considered part of the disclosure of this application, and is incorporated in its entirety into this application.
| Number | Date | Country | |
|---|---|---|---|
| 63401870 | Aug 2022 | US |
