SYSTEMS AND METHODS FOR DETECTING PRESENCE OF LEAKS IN BEDS
Disclosed herein are systems, methods, and techniques for detecting presence of leaks and/or holes in bed systems. An example system can include a bed system having a mattress to support a user laying on the bed system, at least one sensor that can be configured to collect pressure data on the bed system, and a computer system having a processor and memory. The computer system can be configured to: receive the pressure data that is collected by the at least one sensor, provide, as input, the pressure data to a model to detect presence of a leak in the mattress of the bed system based at least in part on the pressure data, receive, as output from the model, data indicating the detected presence of a leak in the mattress, and return a message indicating the detected presence of a leak in the mattress.
This application claims the benefit of U.S. Provisional Application Ser. No. 63/394,423, filed Aug. 2, 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.
The present document relates to detecting presence of a leak in a bed.
BACKGROUNDIn 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.
SUMMARYThe present disclosure generally relates to systems and methods for detecting leaks in bed systems. More particularly, the techniques described herein can be used to determine whether a bed has a leak from a pressure drop. Machine learning techniques can be used to determine whether a bed, such as an air mattress in a bed system, has one or more holes that cause air to leak from the bed. Additionally or alternatively, machine learning techniques can be used to determine a rate at which air leaks from the bed, thereby indicating presence of a leak in the bed. Machine learning trained models and algorithms, including but not limited to linear models, logistic regression models, support vector machine (SVM) models, other polynomial models, physics-based models, and/or neural network models can be trained to accurately detect leaks in bed systems. Leaks may also be detected in bed systems using rulesets and predetermined thresholds. In some implementations, fuzzy logic may also be used to detect leaks in bed systems.
As an illustrative example, the bed system can be inflated to a predetermined pressure level (e.g., a maximum pressure setting) and pressure data can be collected from the bed system over a predetermined period of time. The pressure data can be transmitted to a computer system, which can analyze the pressure data to determine whether the bed system has a leak. The computer system can determine whether the bed system has a leak based on identifying, from the pressure data, whether a hole is present in the bed system. For example, the computer system can detect a pressure reduction that is indicative of a hole or otherwise consistent with a hole being present in the bed system. The computer system can also determine whether the bed system has a leak based on identifying, from the pressure data, a leak rate of the bed system. Any of such determinations can be made by providing the pressure data as input to any of the machine learning trained models described above. The model(s) can output an indication of whether a leak is detected in the bed system based on the pressure data. This output can also be used by the computer system to generate a confidence value of whether the bed system has the leak. Using the output and/or the confidence value, the computer system can generate one or more service actions in order to fix the detected leak in the bed system.
The disclosed techniques can be used when the bed system is initially set up in a user's home. For example, the user can order the bed system and a technician can install the bed system in the user's home. As part of installation, the technician can set up the bed system and run a diagnostic test to check whether the bed system has any leaks. The diagnostic test can include adjusting a pressure of the bed system to a maximum pressure setting such that sensors of the bed system can collect pressure data once the bed system is set to the maximum pressure setting. This pressure data can be used to determine whether the bed system has a leak or leaks. The disclosed techniques can also be used whenever the bed system is already set up and in use by the user. For example, the user may experience or perceive air loss in their bed system and can call customer service to help diagnose the issue. A customer service agent can run a leak detection test remotely as part of a troubleshooting procedure. As another example, the user may desire to check whether the bed system has a leak at various times, such as once a week, after several months of having the bed system, or whenever else the user desires. For example, the user may believe that the bed system is leaking and thus may instigate the leak test to be performed. Environmental conditions, such as a rapid change in air temperature and/or barometric pressure due to weather patterns, can cause changes to the pressure in the bed system. These changes in pressure in the bed system can cause the user to believe the bed system is leaking. Therefore, the disclosed techniques can be performed to determine whether the bed system is in fact leaking or whether changes in the environment simply cause the pressure level to change inside the bed system.
Some embodiments described herein include a system including: a bed system having a mattress to support a user laying on the bed system, at least one sensor that can be configured to collect pressure data on the bed system, and a computer system having a processor and memory. The computer system can be configured to: receive the pressure data that is collected by the at least one sensor, provide, as input, the pressure data to a model to detect presence of a leak in the mattress of the bed system based at least in part on the pressure data, receive, as output from the model, data indicating the detected presence of a leak in the mattress, and return a message indicating the detected presence of a leak in the mattress.
Embodiments described herein can include one or more optional features. For example, the model can be a linear model. The model can be a polynomial model including at least one of a logistic regression model and a support vector machine (SVM) model. The model can be a physics-based model that may include variables for volume data and the pressure data of the bed system. The physics-based model can be configured to determine a hole diameter value of the leak in the mattress based at least in part on the volume data and the pressure data. The model can also be a neural network model.
As another example, the computer system can be configured to receive the pressure data in response to: receiving an indication from a user device to initiate a leak detection test for the bed and transmitting a signal to a pump of the bed system to inflate the mattress of the bed system. The signal to the pump can include instructions that, when executed by the pump, cause the pump to inflate the mattress to a predetermined amount of inflation. The signal to the pump can also include instructions that, when executed by the pump, cause the pump to inflate the mattress to an amount of inflation that can be a predetermined multiplication factor of a predetermined amount of inflation of the mattress. In some implementations, the predetermined multiple factor can be at least one of 2 times the predetermined amount of inflation, 1.5 times the predetermined amount of inflation, and 1.75 times the predetermined amount of inflation. Moreover, the user device can be a technician device used by a technician that sets up the bed system for the user of the bed system. The user device can also be used by the user of the bed system, and the indication from the user device can be received after the bed system has been set up.
In some implementations, the bed system further can include a pump that can be configured to inflate and deflate the mattress, the at least one sensor being in fluid communication with the pump. The mattress can also include at least one air chamber, the at least one sensor being a pressure sensor in fluid communication with the air chamber. The bed system further can include means for controlling pressure of the mattress that can include the at least one sensor. The computer system can also determine a leak confidence value based on the data indicating the detected presence of a leak in the mattress.
As another example, the computer system can be configured to: determine, based on initial pressure data sensed by the at least one sensor, whether a foundation of the bed system is flat, determine, based on the initial pressure data sensed by the at least one sensor, whether the user is laying on the mattress, and poll, based on a determination that the foundation is flat and the user is not laying on the mattress, the at least one sensor for the pressure data. The computer system can also be configured to: control the bed system to adjust the foundation to a flat position based on a determination that the foundation is not flat and poll, in response to adjusting the foundation to the flat position, the at least one sensor for the pressure data. The computer system can also store, in local memory of the computer system, current settings of the bed system before controlling the bed system to adjust the foundation to the flat position, the current settings corresponding to a state of the bed system. The state of the bed system can include at least one of (i) a position of the foundation, (ii) a firmness setting of the mattress, (iii) a responsive air setting for the bed system, and (iv) an activation of a heating or cooling feature of the bed system. Moreover, the computer system can be configured to: retrieve, from the local memory and after returning the detected presence of the leak in the mattress, the stored settings of the bed system, and control the bed system to adjust to the stored settings.
In some implementations, the computer system can also be configured to: determine, based on the pressure data, a leak rate for the mattress of the bed system, determine whether the leak rate exceeds a threshold leak rate value, generate a leak presence indication for the bed system based on a determination that the leak rate exceeds the threshold leak rate value, and return the leak presence indication. The detected presence of the leak can have a linear relationship with the leak presence indication, the detected presence of the leak corresponding to presence of a hole in the mattress and the leak presence indication corresponding to a rate at which air leaks out of the mattress. Sometimes, the detected presence of the leak can have a linear relationship with the leak presence indication, the detected presence of the leak corresponding to presence of a hole in a connection hose of the bed system and the leak presence indication corresponding to a rate at which air leaks out of the connection hose. Sometimes, the detected presence of the leak can have a linear relationship with the leak presence indication, the detected presence of the leak corresponding to presence of a hole in a valve of at least one of a pump of the bed system and an air chamber of the bed system, and the leak presence indication corresponding to a rate at which air leaks out of the valve.
In some implementations, the computer system can also process the received pressure data, in which processing the received pressure data may include discarding a portion of the received pressure data that corresponds to a predetermined amount of time at which the at least one sensor begins to collect the pressure data. During the predetermined amount of time at which the at least one sensor begins to collect the pressure data, a pump of the bed system can inflate the mattress to a predetermined amount of inflation. The computer system can also be configured to receive the pressure data that is collected by the at least one sensor for a predetermined amount of time. The predetermined amount of time can be a sleep session of the user. The predetermined amount of time can be one or more sleep sessions of the user. The predetermined amount of time can be 5 minutes. The predetermined amount of time can be 24 hours. The predetermined amount of time can be an amount of time between consecutive sleep sessions of the user.
Sometimes, the computer system can generate output to be presented in a graphical user interface (GUI) display to a user that may include the data indicating the detected presence of a leak in the mattress. The computer system can also determine a service action for the bed system based on the data indicating the detected presence of a leak in the bed system. The service action can include ordering a component to fix the leak in the bed system. The service action may include scheduling a technician to fix the leak in the bed system.
Moreover, the computer system can be a cloud-based server that may be remote from the bed system. The computer system can sometimes be a controller of the bed system. The at least one sensor can be a pressure sensor. In some implementations, the model was trained, using machine learning techniques, to detect the presence of a leak in the mattress based on training data that may include estimated sizes of holes in bed systems. As another example, the model was trained, using machine learning techniques, to detect a side of the mattress having the leak based on a gas leak rate equation. The computer system can also determine a type of the mattress based on information about the bed system, the information being received from at least one of a data store and a user device during setup of the bed system, select a model to detect a leak in mattresses of the same type as the mattress, and provide, as input, the received pressure data to the selected model. In some implementations, the system can also include a controller in communication with the at least one sensor and the computer system, the controller being configured to instruct the at least one sensor to collect the pressure data. Sometimes, the model was trained using machine learning techniques.
One or more embodiments described herein include a system having a bed system with a mattress to support a user laying on the bed system, at least one sensor that can be configured to collect pressure data on the bed system, and a computer system having a processor and memory. The computer system can be configured to: receive the pressure data that is collected by the at least one sensor, determine, based on the pressure data, a leak rate for the bed system, determine whether the leak rate exceeds a threshold leak rate value, generate a leak presence indication for the bed system based on a determination that the leak rate exceeds the threshold leak rate value, and return the leak presence indication for presentation in a graphical user interface (GUI) display at a user device.
The system can optionally include one or more of the following features. For example, pressure data can be collected, by the at least one sensor, for a threshold period of time. The threshold period of time can be 5 minutes. Determining, based on the pressure data, the leak rate for the bed system can include determining a change in the pressure data over a threshold amount of time that the pressure data is collected by the at least one sensor. The user device can be a mobile computing device of the user of the bed system. The user device can also be a mobile computing device of a technician setting up the bed system. The user device can be a computing device of a customer service agent who was requested, by the user of the bed system, to initiate a leak detection test at the bed system.
As another example, the computing system may also: provide, as input, the pressure data to a model that was trained using machine learning techniques to detect presence of a leak in the bed system based at least in part on the pressure data, and receive, as output from the model, data indicating detected presence of a leak in the bed system. The computing system can also validate the leak presence indication with the data indicating detected presence of the leak in the bed system to return the leak presence indication. In some implementations, the leak presence indication can indicate a location at which the leak is detected in the bed system, the location being at least one of a mattress, a connection hose between components of the bed system, a valve of a pump of the bed system, and a valve of an air chamber in the mattress of the bed system.
One or more embodiments described herein can include a system having: a bed system having a mattress with an air chamber, the mattress sized and configured to support a user laying on the bed system, at least one sensor that can be configured to collect data on the bed system, and a computer system having a processor and memory, the computer system being configured to: receive the data that is collected by the at least one sensor, provide, as input, the data to a model to detect presence of a leak in the air chamber of the mattress of the bed system based at least in part on the data, receive, as output from the model, data indicating the detected presence of a leak in the mattress, and return a message indicating the detected presence of a leak in the mattress. The system can optionally include one or more of the abovementioned features.
One or more embodiments described herein can include a system having a controller that can be configured to detect presence of a leak in an air camber of a mattress via inputting sensed data to a model to detect presence of a leak in the air chamber of the mattress of the bed system. The system can optionally include one or more of the abovementioned features.
One or more embodiments described herein can include a method including: receiving, pressure data that is collected by at least one sensor of a bed system, providing, as input, the pressure data to a model to detect presence of a leak in the bed system based at least in part on the pressure data, receiving, as output from the model, data indicating the detected presence of a leak in bed system, and returning a message indicating the detected presence of a leak in the bed system.
The method can optionally include one or more of the abovementioned features. The method can also optionally include one or more of the following features. For example, the method can also include receiving the pressure data in response to: receiving an indication from a user device to initiate a leak detection test for the bed system, and transmitting a signal to a pump of the bed system to inflate a mattress of the bed system to a threshold amount of inflation. The threshold amount of inflation can be a highest amount of inflation during runtime use of the bed system by a user.
The method may also include determining a leak confidence value based on the data indicating the detected presence of a leak in the bed system. The leak in the bed system can be at least one of a hole in a mattress of the bed system, a hole in a connection hose between components of the bed system, a hole in a valve of a pump of the bed system, and a hole in a valve of an air chamber of the bed system. The method may also include; determining, based on initial pressure data sensed by the at least one sensor, whether a foundation of the bed system is flat, determining, based on the initial pressure data sensed by the at least one sensor, whether a user is laying on a mattress of the bed system, and polling, based on a determination that the foundation is flat and the user is not laying on the mattress, the at least one sensor for the pressure data. The method may also include controlling the bed system to adjust the foundation to a flat position based on a determination that the foundation is not flat, and polling, in response to adjusting the foundation to the flat position, the at least one sensor for the pressure data. Moreover, the method can include determining, based on the pressure data, a leak rate for the bed system, determining whether the leak rate exceeds a threshold leak rate value, generating a leak presence indication for the bed system based on a determination that the leak rate exceeds the threshold leak rate value, and returning the leak presence indication.
One or more embodiments described herein can include a method including: receiving pressure data that is collected by at least one sensor of a bed system, determining, based on the pressure data, a leak rate for the bed system, determining whether the leak rate exceeds a threshold leak rate value, generating a leak presence indication for the bed system based on a determination that the leak rate exceeds the threshold leak rate value, and returning the leak presence indication for presentation in a graphical user interface (GUI) display at a user device.
The method can optionally include one or more of the abovementioned features. The method can also optionally include one or more of the following features. For example, determining, based on the pressure data, the leak rate for the bed system can include determining a change in the pressure data over a threshold amount of time that the pressure data is collected by the at least one sensor. The method may also include providing, as input, the pressure data to a model that was trained using machine learning techniques to detect presence of a leak in the bed system based at least in part on the pressure data, and receiving, as output from the model, data indicating detected presence of a leak in the bed system.
The devices, system, and techniques described herein may provide one or more of the following advantages. For example, using machine learning models can provide for improved accuracy in detecting leaks in bed systems. The models can be trained with robust training datasets to accurately detect slow and/or fast leaks in bed systems. The models can be used to detect leaks in beds at different times, whether the beds are initially set up and/or while the beds are already set up and in use by users. As a result, leaks can be accurately detected early enough to service the beds and remedy the leaks so that the users can continue to experience quality sleep. Servicing the beds early enough, such as when the beds are initially set up, can also reduce costs and an amount of material/equipment that may be needed to remedy the leaks.
Similarly, leak detection using the disclosed techniques can provide for determining service operations in advance, before the leaks become more serious issues that require extensive costs, time, and equipment to fix. As a result of the disclosed techniques, leaks can be fixed when the bed system is initially set up in a user's home and before the user uses the bed system.
Moreover, performing the disclosed techniques can be beneficial to ensure that service operations are not prematurely performed on the bed system. Diagnosing the bed system for a potential leak can be a time consuming process and may pose additional burden on users by relying on them to isolate an air chamber in the bed system (e.g., by disconnecting a pump and capping hoses) and monitor the air chamber for a few days to determine whether a leak is actually present. In many cases, pumps and/or air chambers may be replaced even without waiting for the user to perform the abovementioned tests, which can result in unnecessary and costly warranty replacements. Sometimes, the user may perceive that the bed system is leaking when in reality, changes in environmental conditions cause the bed system to lower in pressure (e.g., a storm moves in and causes sudden changes in ambient air temperature and/or pressure). In some implementations, the user may perceive loss in pressure after a night's sleep on the bed by the user. A slow leak, for example, may be noticeable when at least a threshold amount of weight of a user or users is on the bed for at least a threshold amount of time. The disclosed techniques can therefore be performed whenever the user perceives that the bed system is leaking in order to determine whether the bed system is in fact leaking. The disclosed techniques can also be performed quickly and accurately without burdening the user to perform various actions to test the bed system themselves. The disclosed techniques allow for leak detection testing to be run remotely (e.g., by a computing system of a customer service agent, by a user device of the user) to quickly and accurately determine whether there is a leak (leading to appropriate replacement of equipment) or not (in which case other solutions may be considered to address the user's perception of air loss). The disclosed leak detection testing may be run at components of the bed system (e.g., a controller of the bed system), a mobile application presented at a device of the user (e.g., mobile phone), and/or in a cloud-based computing system. Accordingly, if the bed system is in fact leaking, then service operations can be appropriately performed to address the leak. If the bed system is not actually leaking, then the costs, time, and equipment of replacing parts of the bed system can be saved, as mentioned above.
As another example, the disclosed techniques can provide for collecting data over long periods of time, which can be beneficial to detect slow and/or small leaks in a bed system. Shorter periods of time for data collection can be used to accurately detect fast and/or large leaks in the bed system (such as during initial set up of the bed system) while longer periods of time for data collection can be used to accurately detect slow and/or small leaks in the bed system (such as during long term use of the bed system). Pressure data can therefore be collected whenever the bed system is not in use by users to determine whether the bed system has any leaks. As a result, the bed system can be continuously checked for any type of leak development. As soon as a leak is detected, a service action can be generated to fix the leak instead of letting the leak get larger over time and more of an issue.
The operational processes of the disclosed technology also can provide a number of technological details that allow computing devices to operate more effectively and more efficiently. For example, data already available to a bed system, such as a smart bed (e.g., pressure data), can also be repurposed to perform the operations described herein. This allows for greater functionality without network overhead, for example. By applying existing hardware of the bed system to a new problem (e.g., leak detection) and producing a new solution, the disclosed technology can cause the bed system to be a better and more versatile sensor.
The disclosed technology can work with bed and computing hardware purchased and used for other purposes. For example, a user may select a pressure adjustable air-bed that includes an air bladder, pressure sensor, load-cells, and controller for the added comfort that such a bed provides over other beds. This hardware can perform double-duty, such as providing for leak detection in the bed without requiring more or additional hardware than was already going to be in use. This can reduce costs and extend functionality of the bed to those unable or unwilling to use single-purpose hardware. In some cases, the user can modify their bed once with a pressure-sensing pad or strip placed under a mattress and then not need to think about or separately store the hardware added to their bed to perform the disclosed techniques.
Moreover, the disclosed technology can be configured to use a minimum number of sensors, and also expandable to add more sensors if they are available and/or desired by various stakeholders, including but not limited to engineers, manufacturers, customer service agents, technicians, and/or users of the bed system. Additional data sources can be incorporated into the bed system to increase accuracy and redundancy of leak detection, especially in case one or more sensing modalities fail.
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.
DETAILED DESCRIPTIONThe disclosed technology provides for detecting presence of leaks in a bed system. One or more machine learning trained models can be used to detect leaks in the bed system when the bed system is initially set up and/or when the bed system is already set up and in use. When the bed system is initially set up, the disclosed techniques can be used to detect presence of large and/or fast leaks. When the bed system is already set up, the disclosed techniques can be used to collect pressure data over time and to detect presence of small and/or slow leaks. When a leak is detected in the bed system, the disclosed techniques can be used to generate service actions that can be performed to remedy the leak. As a result, the leak can be fixed to avoid additional damage and/or issues with the bed system. If a leak is not detected in the bed system, then other actions can be generated to diagnose an issue with the bed system that may cause the bed system to appear or feel as if it has a leak (e.g., rapid changes in environment temperature and/or barometric pressure can cause the pressure to drop in the bed system and give off the perception that the bed system has a leak).
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. For example, as mentioned above, the bed 112 can be controlled by a mobile device in wired or wireless communication with the bed 112.
The pump 120 and the remote control 122 can be in two-way communication with the control box 124. The pump 120 includes a motor 142, a pump manifold 143, a relief valve 144, a first control valve 145A, a second control valve 145B, and a pressure transducer 146. The pump 120 is fluidly connected with the first air chamber 114A and the second air chamber 114B via a first tube 148A and a second tube 148B, respectively. The first and second control valves 145A and 145B can be controlled by switching mechanism 138, and are operable to regulate the flow of fluid between the pump 120 and first and second air chambers 114A and 114B, respectively.
In some implementations, the pump 120 and the control box 124 can be provided and packaged as a single unit. In some implementations, the pump 120 and the control box 124 can be provided as physically separate units. In yet some implementations, 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 example air bed system 100 depicted 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 in order to convey the pressure information to the user. The processor 136 can also send the digital signal to one or more other devices in wired or wireless communication with the air bed system, including but not limited to mobile devices such as smartphones, cellphones, tablets, computers, wearable devices, and home automation devices. As a result, the user can view pressure information associated with the air bed system at their mobile 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 in order to increase the firmness of the chamber, the pressure transducer 146 can sense pressure within the pump manifold 143. Again, 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 in order to convey the pressure information to the user.
Generally speaking, during an inflation or deflation process, the pressure sensed within the pump manifold 143 can provide an approximation of the pressure within the respective air chamber that is in fluid communication with the pump manifold 143. An example method of obtaining a pump manifold pressure reading that is substantially equivalent to the actual pressure within an air chamber includes turning off the pump 120, allowing the pressure within the air chamber 114A or 114B and the pump manifold 143 to equalize, and then sensing the pressure within the pump manifold 143 with the pressure transducer 146. Thus, 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 114A and/or 114B. The pressure sensors can also be fluidly connected to the air chambers 114A and 114B, 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 laying on the bed 112. 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 motion of the user, presence of the user on a surface of the bed 112, weight of the user, heart arrhythmia of the user, snoring of the user or another user on the air bed system, and apnea of the user. 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 on the bed 112 can be beneficial to determine, by the processor 136, one or more adjustments to make to settings of the bed 112 (e.g., adjusting a firmness of the bed 112 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 on the bed 112. As another example, the processor 136 can determine that the user is present on the bed 112 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 on the bed 112. 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 upon the bed.
In some implementations, fluctuations in pressure 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 fluctuations in pressure within the pump 120. The fluctuations in pressure detected at the pump 120 can indicate fluctuations in pressure in one or both of the chambers 114A and 114B. One or more sensors located at the pump 120 can be in fluid communication with one or both of the chambers 114A and 114B, and the sensors can be operative to determine pressure within the chambers 114A and 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.
In some implementations, the control box 124 can 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 the force input applied to the chamber 114A from the user's movement, 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 also receive additional biometric signals of the user from one or more other sensors or sensor arrays that are 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 of the user. The processor 136 can then 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 heartrate, respiratory rate, and other information about the user and the user's 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 heartrate, respiratory rate, or other 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 the user's heartrate, respiratory rate, or other 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. Thus, 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 also attribute such fluctuations in air pressure to sleep quality of the user. Such attributions can be determined based on applying one or more machine learning models and/or algorithms to the pressure signals generated by the pressure transducer 146. 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 data collected by the pressure transducer 146 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 that can be 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 therefore be selectively controlled by the temperature controller to provide cooling or heating that is preferred by each of the users 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 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 then transmit the received information to the remote control 122, where the information can 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 (e.g., smartphone, cellphone, laptop, tablet, computer, wearable device, or home automation 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 a bed (e.g., the bed 112) by adjusting the adjustable foundation that supports 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 be configured to provide different levels of massage to one or more portions of the bed 112 for one or more users on the bed 112. The user(s) can also adjust one or more massage settings for different portions of the bed 112 using the remote control 122 and/or a mobile device in communication with the air bed system 100, as described above.
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. As another example, the control circuitry 334 located within the pump 304 can communicate with control circuitry 334 at a remote location through a LAN or WAN (e.g., the internet). As yet another example, the control circuitry 334 can 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 and/or health related) about the user 308. For example, the bed 302 can include a second pump in addition to the pump 304, with each of the two pumps 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, such as bed presence, sleep state, movement, and biometric signals. The second pump can then 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 that are operable to detect movement, including user presence, user 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 during sleep. The movement detected by the one or more pressure sensitive pads or surface portions can be used by control circuitry 334 to identify user sleep state, bed presence, or biometric signals for each of the users. 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 that are 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 the user 308 falling asleep, total amount of time spent in the bed 302 for a given period of time, heartrate for the user 308 over a period of time, respiration rate for the user 308 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 of the bed 302. 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. For example, the information presented on the user device 310 can evolve with the age of the user 308 such that different information is presented on the user device 310 as the user 308 ages as a child or an adult.
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 more accurately determine overall sleep quality and/or health of the user 308 based on information that is detected by one or more components (e.g., sensors) of the bed 302.
As another example, and as mentioned above, the user 308 can use the user device 310 as an interface for controlling air pressure of the air chambers 306a and 306b, for controlling various recline or incline positions of the bed 302, for controlling 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 in greater detail below).
In some implementations, the control circuitry 334 of the bed 302 can communicate with other devices or systems in addition to or instead of the user device 310. For example, the control circuitry 334 can communicate with the television 312, a lighting system 314, a thermostat 316, a security system 318, home automation devices, and/or other household devices, including but not limited to an oven 322, a coffee maker 324, a lamp 326, and/or a nightlight 328. Other examples of devices and/or systems that the control circuitry 334 can communicate with include a system for controlling window blinds 330, one or more devices for detecting or controlling the 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 of the bed 302 and other devices can occur through a network (e.g., a LAN or the Internet) or as point-to-point communication (e.g., using Bluetooth, radio communication, or a wired connection). In some implementations, control circuitry 334 of different beds 302 can 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. In some implementations, the control signals are 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 one or more 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 one or more other devices, the control circuitry 334 can provide collected information (e.g., information related to user movement, bed presence, sleep state, or biometric signals for the user 308) 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 one or more other devices in the house. The central controller can include one or more 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, etc.). The sensors can detect signals including but not limited to ambient light, temperature, humidity, volatile organic compound(s), pulse, motion, and audio. These signals can be combined with signals that are detected by sensors of the bed 302 to determine more 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, etc.) 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 can output information and also receive input from the user 308. The display can output information such as the user 308's health, sleep quality, weather information, security integration features, lighting integration features, heating and cooling integration features, and other controls to automate devices in the house. The central controller can therefore 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.
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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 experiencing 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 is able to use collected information (including information related to user interaction with the bed 302 by the user 308, as well as environmental information, time information, and input received from the user 308) 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.
For example, 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 only temporary, and use this determination to generate different control signals than would be generated if the control circuitry 334 determined that 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 has gotten out of bed at 3:00 am, the control circuitry 334 can use identified patterns for the user 308 to determine that the user has only gotten up temporarily (e.g., to use the bathroom, or get a glass of water) and is not up for the day. For example, the control circuitry 334 can turn on underbed lighting to assist the user 308 in carefully moving around the bed 302 and the room. By contrast, if the control circuitry 334 identifies that the user 308 has gotten out of the bed 302 at 6:40 am, the control circuitry 334 can determine that the user 308 is up for the day and generate a different set of control signals than those that would be generated if it were determined that the user 308 were only getting out of bed temporarily (as would be the case when the user 308 gets out of the bed 302 at 3:00 am) (e.g., the control circuitry 334 can turn on light 326 near the bed 302 and/or raise the window blinds 330 when it is determined that the user 308 is up for the day). 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.
As described above, the control circuitry 334 for the bed 302 can generate control signals for control functions of various other devices. The control signals can be generated, at least in part, based on detected interactions by the user 308 with the bed 302, as well as other information including time, date, temperature, etc. The control circuitry 334 can communicate with the television 312, receive information from the television 312, and generate control signals for controlling functions of the television 312. For example, the control circuitry 334 can receive an indication from the television 312 that the television 312 is currently 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. For example, 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., minutes), the control circuitry 334 can determine that the user 308 is in bed for the evening. If the television 312 is on (as indicated by communications received by the control circuitry 334 of the bed 302 from the television 312), 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 between the television 312 and the control circuitry 334 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 has indicated a preference for watching the morning news upon getting out of bed). The control circuitry 334 can generate the control signal and transmit the signal to the television 312 to cause the television 312 to turn on and tune to the desired station (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., 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. In response to this determination, the control circuitry 334 can 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 signals can then be transmitted to the lighting system 314 and executed by the lighting system 314 to cause the lights in the indicated rooms to shut off. For example, 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 generated by the control circuitry 334 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, in response to determining that the user 308 is in bed for the evening. Additionally, 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. As another example, the control circuitry 334 can 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 in which the bed 302 is located) in response to 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 be configured to 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 of 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). As another example, the control circuitry 334 can 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. For example, the sunrise lighting scheme can include lighting the bedroom with blue light to gently assist the user 308 in waking up and becoming active.
In some implementations, the control circuitry 334 can generate different control signals for controlling actions of one or more components, such as the lighting system 314, 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 for interactions between the user 308 and the bed 302 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 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. For example, if the user 308 gets out of bed prior to 6:30 am, the control circuitry 334 can turn on lights that guide the user 308's route to a bathroom. As another example, if the user 308 gets out of bed prior to 6:30 am, the control circuitry 334 can turn on lights that guide the user 308's route to the kitchen (which can include, for example, turning on the nightlight 328, turning on under bed lighting, turning on the lamp 326, or turning on lights along a path that the user 308 takes to get to the kitchen).
As another example, if the user 308 gets out of bed after 6:30 am, the control circuitry 334 can generate control signals to cause the lighting system 314 to initiate a sunrise lighting scheme, or to turn on one or more lights in the bedroom and/or other rooms. 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 the bathroom, kitchen, or another 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 then 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). In some implementations, buffer time can be added to these timeframes. For example, if the user is identified as typically going to bed between 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 the 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 (such as for the night) as opposed to being present on the bed 302 for a shorter period (such as for a nap) by sensing duration of presence of the user 308 (e.g., by detecting pressure signals and/or temperature signals of the user 308 on the bed 302 by one or more sensors that are 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 (such as for the night) as opposed to going to bed for a shorter period (such as for a nap) by sensing duration of sleep of the user 308. For example, 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 also 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.
In some examples, the control circuitry 334 can automatically determine the bed time range of the user 308 without requiring user inputs. In some examples, the control circuitry 334 can determine the bed time range of the user 308 automatically and in combination with user inputs (e.g., using one or more signals that are sensed by sensors of the bed 302 and/or the central controller described above). In some examples, the control circuitry 334 can set the bed time range directly according to user inputs. In some examples, 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 one or more components (such as the lighting system 314, the thermostat 316, the security system 318, the oven 322, the coffee maker 324, the lamp 326, and the nightlight 328), as a function of sensed bed presence and the bed time range.
The control circuitry 334 can additionally communicate with the thermostat 316, receive information from the thermostat 316, and generate control signals for controlling functions of the thermostat 316. For example, the user 308 can indicate user preferences for different temperatures at different times, depending on the sleep state or bed presence of the user 308. For example, the user 308 may prefer an environmental temperature of 72 degrees when out of bed, 70 degrees when in bed but awake, and 68 degrees when sleeping. The control circuitry 334 of the bed 302 can detect bed presence of the user 308 in the evening and determine that the user 308 is in bed for the night. In response to this determination, the control circuitry 334 can generate control signals to cause the thermostat 316 to change the temperature to 70 degrees. The control circuitry 334 can then transmit the control signals to the thermostat 316. Upon detecting that the user 308 is in bed during the bed time range or asleep, the control circuitry 334 can generate and transmit control signals to cause the thermostat 316 to change the temperature to 68. The next morning, upon determining that the user 308 is awake for the day (e.g., the user 308 gets out of bed after 6:30 am), the control circuitry 334 can generate and transmit control circuitry 334 to cause the thermostat to change the temperature to 72 degrees.
The control circuitry 334 can also determine control signals to be transmitted to the thermostat 316 based on maintaining improved or preferred sleep quality of the user 308. In other words, the control circuitry 334 can determine adjustments to the thermostat 316 that are not merely based on user-inputted preferences. For example, the control circuitry 334 can determine, based on historic sleep patterns and quality of the user 308 and by applying one or more 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 one or more devices and/or sensors in the bedroom indicating a temperature of the bedroom. 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 in the house 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 in the house to lower the temperature back to 74 degrees. Sometimes, the control circuitry 334 can also determine control signals that cause the thermostat 316 to maintain the bedroom within a temperature range that is intended to keep the user 308 in particular sleep states and/or transition to next preferred sleep states.
In some implementations, the control circuitry 334 can generate control signals to cause one or more 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. As yet another example, the user 308 can pre-program various times at which the temperature at the surface of the bed should be raised or lowered. For example, the user 308 can program the bed 302 to raise the surface temperature to 76 degrees at 10:00 pm, and lower the surface temperature to 68 degrees at 11:30 pm. As another example, one or more temperature sensors on the surface of the bed 302 can detect microclimates of the user 308 on the bed 302. When a detected microclimate of the user 308 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 the user 308 in their sleep cycle, transitioning the user 308 to a next preferred sleep state, and/or otherwise maintaining or improving the user 308's sleep quality.
In some implementations, in response to detecting user bed presence of the user 308 and/or that the user 308 is asleep, the control circuitry 334 can cause the thermostat 316 to change the temperature in different rooms to different values. For example, in response to determining that the user 308 is in bed for the evening, the control circuitry 334 can generate and transmit control signals to cause the thermostat 316 to set the temperature in one or more bedrooms of the house to 72 degrees and set the temperature in other rooms to 67 degrees. Other control signals are also possible, and can be based on user preference and user input.
The control circuitry 334 can also receive temperature information from the thermostat 316 and use this temperature information to control functions of the bed 302 or other devices. For example, as discussed above, the control circuitry 334 can adjust temperatures of heating elements included in or otherwise attached to the bed 302 (e.g., a foot warming pad) in response to temperature information received from the thermostat 316.
In some implementations, the control circuitry 334 can generate and transmit control signals for controlling other temperature control systems. For example, in response to determining that the user 308 is awake for the day, the control circuitry 334 can generate and transmit control signals for causing floor heating elements to activate in the bedroom and/or in other rooms in the house. For example, the control circuitry 334 can cause a floor heating system in a master bedroom to turn on in response to determining that the user 308 is awake for the day. One or more of the control signals described herein that are determined by the control circuitry 334 can also be determined by the central controller described above.
The control circuitry 334 can additionally 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. The control circuitry 334 can then transmit the control signals to the security system 318 to cause the security system 318 to engage (e.g., turning on security cameras along a perimeter of the house, automatically locking doors in the house, etc.). 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 after 6:00 am). In some implementations, the control circuitry 334 can generate and transmit a first set of control signals to cause the security system 318 to engage a first set of security features in response to detecting user bed presence of the user 308, and can generate and transmit a second set of control signals to cause the security system 318 to engage a second set of security features in response to detecting that the user 308 has fallen asleep.
In some implementations, the control circuitry 334 can receive alerts from the security system 318 and indicate the alert to the user 308. For example, the control circuitry 334 can detect that the user 308 is in bed for the evening and in response, generate and transmit control signals to cause the security system 318 to engage or disengage. The security system can then detect a security breach (e.g., someone has opened the door 332 without entering the security code, or someone has opened a window when the security system 318 is engaged). The security system 318 can communicate the security breach to the control circuitry 334 of the bed 302. In response to receiving the communication from the security system 318, the control circuitry 334 can generate control signals to alert the user 308 to the security breach. For example, the control circuitry 334 can cause the bed 302 to vibrate. As another example, the control circuitry 334 can cause portions of the bed 302 to articulate (e.g., cause the head section to raise or lower) in order to wake the user 308 and alert the user to the security breach. As another example, the control circuitry 334 can generate and transmit control signals to cause the lamp 326 to flash on and off at regular intervals to alert the user 308 to the security breach. As another example, the control circuitry 334 can alert the user 308 of one bed 302 regarding a security breach in a bedroom of another bed, such as an open window in a kid's bedroom. As another example, the control circuitry 334 can send an alert to a garage door controller (e.g., to close and lock the door). As another example, 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 described above The central controller 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). For example, in response to determining that the user 308 is in bed for the evening, the control circuitry 334 can generate and transmit a request to a garage door opener or another device capable of sensing if the garage door 320 is open. The control circuitry 334 can request information on the current state of the garage door 320. If the control circuitry 334 receives a response (e.g., from the garage door opener) indicating that the garage door 320 is open, the control circuitry 334 can either notify the user 308 that the garage door is open (e.g., by displaying a notification or other message at the user device 310, by outputting a notification at the central controller, etc.), and/or generate a control signal to cause the garage door opener to close the garage door 320. For example, the control circuitry 334 can send a message to the user device 310 indicating that the garage door is open. As another example, the control circuitry 334 can cause the bed 302 to vibrate. As yet another example, the control circuitry 334 can generate and transmit a control signal to cause the lighting system 314 to cause one or more lights in the bedroom to flash to alert the user 308 to check the user device 310 for an alert (in this example, an alert regarding the garage door 320 being open). Alternatively, or additionally, the control circuitry 334 can generate and transmit control signals to cause the garage door opener to close the garage door 320 in response to identifying that the user 308 is in bed for the evening and that the garage door 320 is open. Control signals can also vary depend on the age of the user 308.
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. For example, upon detecting that the user 308 is in bed for the evening, the control circuitry 334 can generate and transmit a request to a device or system for detecting a state of the door 332. Information returned in response to the request can indicate various states of the door 332 such as open, closed but unlocked, or closed and locked. If the door 332 is open or closed but unlocked, the control circuitry 334 can alert the user 308 to the state of the door, such as in a manner described above with reference to the garage door 320. Alternatively, or in addition to alerting the user 308, the control circuitry 334 can generate and transmit control signals to cause the door 332 to lock, or to close and lock. If the door 332 is closed and locked, the control circuitry 334 can determine that no further action is needed.
Similarly, upon detecting that the user 308 is in bed for the evening, the control circuitry 334 can generate and transmit a request to the oven 322 to request a state of the oven 322 (e.g., on or off). If the oven 322 is on, the control circuitry 334 can alert the user 308 and/or generate and transmit control signals to cause the oven 322 to turn off. If the oven is already off, the control circuitry 334 can determine that no further action is necessary. In some implementations, different alerts can be generated for different events. For example, the control circuitry 334 can cause the lamp 326 (or one or more 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., that a child of the user 308 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 that can be processed by the control circuitry 334 of the bed 302 and communicated to the user (e.g., at the user device 310 and/or the central controller described herein) include a smoke detector detecting smoke (and communicating this detection of smoke to the control circuitry 334), a carbon monoxide tester detecting carbon monoxide, a heater malfunctioning, or an alert from any other device capable of communicating with the control circuitry 334 and detecting an occurrence that should be brought 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 in bed for the evening, the control circuitry 334 can generate and transmit control signals to cause the window blinds 330 to close. As another example, in response to determining that the user 308 is up for the day (e.g., user has gotten out of bed after 6:30 am) 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. As yet another example, the control circuitry 334 can generate and transmit control signals that cause a first set of blinds to close in response to detecting user bed presence of the user 308 and a second set of blinds to close in response to detecting that the user 308 is asleep.
The control circuitry 334 can generate and transmit control signals for controlling functions of other household devices in response to detecting user interactions with the bed 302. For example, 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 begin brewing coffee. As another example, the control circuitry 334 can generate and transmit control signals to the oven 322 to cause the oven 322 to begin preheating (for users that like fresh baked bread in the morning or otherwise bake or prepare food in the morning). As another example, 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.
As another example, the control circuitry 334 can generate and transmit control signals to cause one or more devices to enter a sleep mode in response to detecting user bed presence of the user 308, or in response to detecting that the user 308 is asleep. For example, the control circuitry 334 can generate control signals to cause a mobile phone of the user 308 to switch into sleep mode or night mode such that notifications from the mobile phone are muted to not disturb the user 308's sleep. The control circuitry 334 can then transmit the control signals to the mobile phone. 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 mode.
In some implementations, the control circuitry 334 can 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, etc.), the control circuitry 334 can generate and transmit control signals to cause one or more noise cancelation devices to activate. The noise cancelation devices can, for example, be included as part of the bed 302 or located in the bedroom with the bed 302. As another example, 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 with the bed 302. As mentioned throughout, functions of the bed 302 described herein can also be controlled by the user device 310 and/or the central controller (e.g., a hub device or other home automation device that controls multiple different devices in the home). As mentioned above, the bed 302 can include an adjustable foundation and an articulation controller configured to adjust the position of one or more portions of the bed 302 by adjusting the adjustable foundation that supports the bed 302. 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). In some implementations, the bed 302 includes multiple separately articulable sections. For example, 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 positioned on the bed 302 surface 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). In some implementations, 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 be configured to provide different levels of massage to one or more users on the bed 302 or to 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 of the bed 302) in response to user interactions with the bed 302. For example, the control circuitry 334 can cause the articulation controller to adjust the bed 302 to a first recline position for the user 308 in response to sensing user bed presence for the user 308. 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 position of the bed 302 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 that the user 308 wishes to go to sleep).
In some implementations, the control circuitry 334 can control the articulation controller so as to wake up one user of the bed 302 without waking another user of the bed 302. For example, the user 308 and a second user of the bed 302 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 to wake the user 308 without disturbing the second user. When the wakeup time for the second user is reached, the control circuitry 334 can cause the articulation controller to vibrate or change the position of only the side of the bed on which the second user is located. Alternatively, when the second wakeup time occurs, the control circuitry 334 can utilize other methods (such as audio alarms, or turning on the lights) to wake the second user since the user 308 is already awake and therefore will not be disturbed when the control circuitry 334 attempts to wake the second user.
Still referring to
Examples of Data Processing Systems Associated with a Bed
Described here are examples of systems and components that can be used 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 of these 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 or 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. For example, connections with power supplies and/or computer readable memory may not be shown for clarities sake, as many or all elements of a particular component may need to be connected to the power supplies and/or computer readable memory.
In this example, a pump motherboard 402 and a pump daughterboard 404 are communicably coupled. They can be conceptually described as a center or hub of the system 400, with the other components conceptually described as spokes of the system 400. In some configurations, this can mean that each of the spoke components communicates primarily or exclusively with the pump motherboard 402. For example, a sensor of the sensor array 406 may not be configured to, or may not be able to, communicate directly with a corresponding controller. Instead, each spoke component can communicate with the motherboard 402. The sensor of the sensor array 406 can report a sensor reading to the motherboard 402, and the motherboard 402 can determine that, in response, a controller of the controller array 408 should adjust some parameters of a logic controlled device or otherwise modify a state of one or more peripheral devices. In one case, if the temperature of the bed is determined to be too hot based on received temperature signals from the sensor array 406, the pump motherboard 402 can determine that a temperature controller should cool the bed.
One advantage of a hub-and-spoke network configuration, sometimes also referred to as 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 may only be transmitted over one spoke of the network to the motherboard 402. The motherboard 402 can, for example, marshal that data and condense it 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 of the network in response to the large stream. For example, if the large stream of data is a pressure reading that is 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 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 can accommodate components being added, removed, failing, etc. This can allow, for example, 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 of the sensor, the system 400 can be configured such that only the motherboard 402 needs to be updated about the replacement sensor. This can allow, for example, 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. Then, 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 will be further discussed. In some alternatives, two or more of the 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.
Previously, a number of cloud services 410 were described. As shown in
Additionally, some or all of the cloud services 410 may be configured to communicate with other cloud services. This communication may include 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, for example, for purposes of backup, coordination, migration, or for performance of calculations or data mining. In another example, many cloud services 410 may 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 can include, for example, 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 for performing the functionality needed.
The memory 512 is generally one or more devices for storing data. The memory 512 can 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 and, in response, control the functioning of the pump motor 506. For example, the pump controller 504 can receive, from the processor 502, 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 is configured to pump air into the selected air chamber, and can engage the pump motor 506 for a length of time that corresponds to PSI or until a sensor indicates that pressure has been increased by 0.3 PSI. In an alternative configuration, 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.
In addition to the power supply 500, processor 502, pump controller 504, pump motor 506, and valve solenoid 508, this motherboard 402 is shown with 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.
Similar to the way that the pump controller 504 converts commands from the processor 502 into control signals for the pump motor 506, the valve controller 600 can convert commands from the processor 502 into control signals for the valve solenoid 508. In one 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 that 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 is shown with 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 the pressure of an air chamber or chambers of an air bed. From this data, the processor 702 can perform an algorithm to calculate a sleep metric (e.g., sleep quality, whether a user is presently in the bed, whether the user has fallen asleep, a heartrate of the user, a respiration rate of the user, movement of the user, etc.). In some examples, the sleep metric can be calculated from only the pressure of air chambers. In other examples, the sleep metric can be calculated using signals from a variety of sensors (e.g., a movement sensor, a pressure sensor, a temperature sensor, and/or an audio sensor). In an example in which different data is needed, the processor 702 can receive that data from an appropriate sensor or sensors. These sensors can 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 then generate instructions for outputting the sleep metric to the user or otherwise using the sleep metric to determine one or more other information about the user or controls to control the bed system and/or peripheral devices.
The peripheral sensors 902, 904, 906, 908, 910, etc. of the sensor array 406 can communicate with the motherboard 402 through one or more of the network interfaces of the motherboard, including but not limited to the USB stack 604, WiFi radio 606, Bluetooth Low Energy (BLE) radio 608, ZigBee radio 610, and Bluetooth radio 612, as is appropriate for the configuration of the particular sensor. For example, a sensor that outputs a reading over a USB cable can communicate through the USB stack 604.
Some of the peripheral sensors of the sensor array 406 can be bed mounted sensors 900, such as a temperature sensor 906, a light sensor 908, and a sound sensor 910. The bed mounted sensors 900 can be, for example, embedded into the structure of a bed and sold with the bed, or later affixed to the structure of the bed (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 of the bed, attached along connecting tubes between a pump and air chambers, within air chambers, attached to a headboard of the bed, attached to one or more regions of an adjustable foundation, etc.). Other sensors 902 and 904 can be in communication with the motherboard 402, but optionally not mounted to the bed. The other sensors 902 and 904 can include a pressure sensor 902 and/or peripheral sensor 904. For example, the sensors 902 and 904 can be integrated or otherwise part of a user mobile device (e.g., mobile phone, wearable device, etc.). The sensors 902 and 904 can also be part of a central controller for controlling the bed and peripheral devices in the home. Sometimes, the sensors 902 and 904 can also be part of one or more home automation devices or other peripheral devices in the home.
In some cases, some or all of the bed mounted sensors 900 and/or sensors 902 and 904 can share networking hardware, including a conduit that contains wires from each sensor, a multi-wire cable or plug that, when affixed to the motherboard 402, connect all of the associated sensors with the motherboard 402. In some embodiments, one, some, or all of sensors 902, 904, 906, 908, and 910 can sense one or more features of a mattress, such as pressure, temperature, light, sound, and/or one or more other features of the mattress. In some embodiments, one, some, or all of sensors 902, 904, 906, 908, and 910 can sense one or more features external to the mattress. In some embodiments, pressure sensor 902 can sense pressure of the mattress while some or all of sensors 902, 904, 906, 908, and 910 can sense one or more features of the mattress and/or external to the mattress.
The peripheral controllers of the controller array 408 can communicate with the motherboard 402 through one or more of the network interfaces of the motherboard, including but not limited to the USB stack 604, WiFi radio 606, Bluetooth Low Energy (BLE) radio 608, ZigBee radio 610, and Bluetooth radio 612, as is appropriate for the configuration of the particular sensor. For example, a controller that receives a command over a USB cable can communicate through the USB stack 604.
Some of the controllers of the controller array 408 can be bed mounted controllers 1000, such as a temperature controller 1006, a light controller 1008, and a speaker controller 1010. The bed mounting controllers 1000 can be, for example, embedded into the structure of a bed and sold with the bed, or later affixed to the structure of the bed, as described in reference to the peripheral sensors in
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, for example, speakers 1106, a touchscreen 1108, or other not shown components, such as a pointing device or keyboard. The computing device 412 can run one or more applications 1110. These applications can include, 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 that are detected based on signals that are sensed at the bed), and/or configure the behavior of the system 400 (e.g., set a desired firmness to the bed, set desired behavior for peripheral devices). In some cases, the computing device 412 can be used in addition to, or to replace, the remote control 122 described previously.
The bed data cloud service 410a is shown with a network interface 1200, a communication manager 1202, server hardware 1204, and server system software 1206. In addition, the bed data cloud service 410a is 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 generally includes hardware and low level software used to allow one or more hardware devices to communicate over networks. For example the network interface 1200 can include network cards, routers, modems, and other hardware needed to allow the components of the bed data cloud service 410a to communicate with each other and other destinations over, for example, the Internet 412.
The communication manager 1202 generally comprises hardware and software that operate above the network interface 1200. This includes software to initiate, maintain, and tear down network communications used by the bed data cloud service 410a. This includes, for example, 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 bed data cloud service 410a.
The server hardware 1204 generally includes physical processing devices used to instantiate and maintain the bed data cloud 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. The server system software 1206 can include 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. For example, the users can include customers, owners, or other users registered with the bed data cloud service 410a or another service. Each user can have, for example, 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. For example, the beds can include products sold or registered with a system associated with the bed data cloud service 410a. Each bed can have, for example, a unique identifier, model and/or serial number, sales information, geographic information, delivery information, a listing of associated sensors and control peripherals, etc. Additionally, an index or indexes stored by the bed data cloud service 410a can identify users that are associated with beds. For example, 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 a temperature sensor, pressure sensor, motion sensor, audio sensor, and/or light sensor. Readings from one or more of these sensors, either in raw form or in a format generated from the raw data (e.g. sleep metrics) of the sensors, can be communicated by the bed's data processing system to the bed data cloud service 410a for storage in the sensor data 1212. Additionally, an index or indexes stored by the bed data cloud service 410a can identify users and/or beds that are associated with the sensor data 1212.
The bed data cloud service 410a can use any of its available data, such as the sensor data 1212, to generate advanced sleep data 1214. In general, the advanced sleep data 1214 includes sleep metrics and other data generated from sensor readings, such as health information associated with the user of a particular bed. Some of these calculations can be performed in the bed data cloud service 410a instead of locally on the bed's data processing system, for example, 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 and still be part of a system that performs relatively complex tasks and computations.
For example, the bed data cloud 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 bed data cloud service 410a can retrieve different types of models based on a type of the advanced sleep data 1214 that is being generated. As an illustrative example, the bed data cloud 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 (e.g., which can be stored in and accessed from a data store). The bed data cloud service 410a can retrieve one or more other models to determine whether the user is currently snoring based on the detected sensor data 1212. The bed data cloud service 410a can also retrieve one or more other models that can be used to determine whether the user is experiencing some health condition based on the detected sensor data 1212.
The sleep data cloud service 410b is shown with a network interface 1300, a communication manager 1302, server hardware 1304, and server system software 1306. In addition, the sleep data cloud service 410b is shown with a user identification module 1308, a pressure sensor manager 1310, a pressure based sleep data module 1312, a raw pressure sensor data module 1314, and a non-pressure sleep data module 1316. Sometimes, the sleep data cloud service 410b can include a sensor manager for each of the sensors that are integrated or otherwise in communication with the bed. In some implementations, the sleep data cloud service 410b can include a sensor manager that relates to multiple sensors in beds. For example, a single sensor manager can relate to pressure, temperature, light, movement, and audio sensors in a bed.
Referring to the sleep data cloud service 410b in
The pressure based sleep data 1312 can use raw pressure sensor data 1314 to calculate sleep metrics specifically tied to pressure sensor data. For example, user presence, movements, weight change, heartrate, and breathing rate can all be determined from raw pressure sensor data 1314. Additionally, an index or indexes stored by the sleep data cloud service 410b can identify users that are 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. For example, user-entered preferences, light sensor readings, and sound sensor readings can all be used to track sleep data. Additionally, an index or indexes stored by the sleep data cloud service 410b can identify users that are associated with other sensors and/or non-pressure sleep data 1316.
The user account cloud service 410c is shown with a network interface 1400, a communication manager 1402, server hardware 1404, and server system software 1406. In addition, the user account cloud service 410c is shown with a user identification module 1408, a purchase history module 1410, an engagement module 1412, and an application usage history module 1414.
The user identification module 1408 can include, or reference, data related to users of beds with associated data processing systems. For example, the users can include customers, owners, or other users registered with the user account cloud service 410c or another service. Each user can have, for example, a unique identifier, and user credentials, demographic information, or any other technologically appropriate information. Each user can also have user-inputted preferences pertaining to the user's bed system (e.g., firmness settings, heating/cooling settings, inclined and/or declined positions of different regions of the bed, etc.), ambient environment (e.g., lighting, temperature, etc.), and/or peripheral devices (e.g., turning on or off a television, coffee maker, security system, alarm clock, etc.).
The purchase history module 1410 can include, or reference, data related to purchases by users. For example, the purchase data can include a sale's contact information, billing information, and salesperson information that is associated with the user's purchase of the bed system. Additionally, an index or indexes stored by the user account cloud service 410c can identify users that are associated with a purchase of the bed system.
The engagement 1412 can track user interactions with the manufacturer, vendor, and/or manager of the bed and or cloud services. This engagement data can include communications (e.g., emails, service calls), data from sales (e.g., sales receipts, configuration logs), and social network interactions. The engagement 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 one or more applications and/or remote controls of a bed. For example, a monitoring and configuration application can be distributed to run on, for example, computing devices 412. The computing devices 412 can include a mobile phone, laptop, tablet, computer, smartphone, and/or wearable device of the user. The computing devices 412 can also include a central controller or hub device that can be used to control operations of the bed system and one or more peripheral devices. Moreover, the computing devices 412 can include a home automation device. The application that is presented to the user via the computing devices 412 can log and report user interactions for storage in the application usage history module 1414. Additionally, an index or indexes stored by the user account cloud service 410c can identify users that are associated with each log entry. User interactions that are stored in the application usage history module 1414 can optionally be used to determine or otherwise 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 point of sale cloud service 1500 is shown with a network interface 1502, a communication manager 1504, server hardware 1506, and server system software 1508. In addition, the point of sale cloud service 1500 is shown with a user identification module 1510, a purchase history module 1512, and a bed setup module 1514.
The purchase history module 1512 can include, or reference, data related to purchases made by users identified in the user identification module 1510. The purchase information can include, for example, data of a sale, price, and location of sale, delivery address, and configuration options selected by the users at the time of sale. These configuration options can include selections made by the user about how they wish their newly purchased beds to be setup and can include, for example, 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, for example, 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 or names of a user or users who will sleep on the bed, which side of the bed each user will use, etc.
Data recorded in the point of sale cloud service 1500 can be referenced by a user's bed system at later dates to control functionality of the bed system and/or to send control signals to peripheral components according to data recorded in the point of sale cloud service 1500. This can allow a salesperson to collect information from the user at the point of sale that later facilitates automation of the bed system. In some examples, some or all aspects of the bed system can be automated with little or no user-entered data required after the point of sale. In other examples, data recorded in the point of sale cloud service 1500 can be used in connection with a variety of additional data gathered from user-entered data.
The environment cloud service 1600 is shown with a network interface 1602, a communication manager 1604, server hardware 1606, and server system software 1608. In addition, the environment cloud service 1600 is shown with a user identification module 1610, an environmental sensors module 1612, and an environmental factors module 1614.
The environmental sensors module 1612 can include a listing and identification of sensors that users identified in the user identification module 1610 have installed in and/or surrounding their bed. These sensors may include any sensors that can detect environmental variables, including but not limited to light sensors, noise/audio sensors, vibration sensors, thermostats, movement sensors (e.g., motion), etc. Additionally, the environmental sensors module 1612 can store historical readings or reports from those sensors. The environmental sensors module 1612 can then be accessed at a later time and used by one or more of the cloud services described herein to determine sleep quality and/or health information of the users.
The environmental factors module 1614 can include reports generated based on data in the environmental sensors module 1612. For example, the environmental factors module 1614 can generate and retain a report indicating frequency and duration of instances of increased lighting when the user is asleep based on light sensor data that is stored in the environment sensors module 1612.
In the examples discussed here, each cloud service 410 is shown with some of the same components. In various configurations, these same components can be partially or wholly shared between services, or they can be separate. In some configurations, each service can have separate copies of some or all of the components that are the same or different in some ways. Additionally, these components are only provided as illustrative examples. In other examples, each cloud service can have different number, types, and styles of components that are technically possible.
In general, the behavior analysis module 1700 can collect data from a wide variety of sources (e.g., sensors 902, 904, 906, 908, and/or 910, non-sensor local sources 1704, cloud data services 410a and/or 410c) and use a behavioral algorithm 1702 (e.g., one or more machine learning models) to generate one or more actions to be taken (e.g., commands to send to peripheral controllers, data to send to cloud services, such as the bed data cloud 410a and/or the user account cloud 410c). This can be useful, for example, in tracking user behavior and automating devices in communication with the user's bed.
The behavior analysis module 1700 can collect data from any technologically appropriate source, for example, to gather data about features of a bed, the bed's environment, and/or the bed's users. Some such sources include any of the sensors of the sensor array 406 that is previously described (e.g., including but not limited to sensors such as 902, 904, 906, 908, and/or 910). For example, this data can provide the behavior analysis module 1700 with information about a current state of the environment around the bed. For example, the behavior analysis module 1700 can access readings from the pressure sensor 902 to determine the pressure of an air chamber in the bed. From this reading, and potentially other data, user presence in the bed can be determined. In another example, the behavior analysis module 1700 can access the light sensor 908 to detect the amount of light in the bed's environment. The behavior analysis module 1700 can also access the temperature sensor 906 to detect a temperature in the bed's environment and/or one or more microclimates in the bed. Using this data, the behavior analysis module 1700 can determine whether temperature adjustments should be made to the bed's environment and/or components of the bed in order to improve the user's sleep quality and overall comfortability.
Similarly, the behavior analysis module 1700 can access data from cloud services and use such data to make more accurate determinations of user sleep quality, health information, and/or control of the user's 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. Other cloud services 410, including those previously described can be accessed by the behavior analysis module 1700. For example, the behavior analysis module 1700 can 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 that is retrieved from the cloud services 410, the behavior analysis module 1700 can more accurately determine user sleep quality, health information, and/or control of the user's bed and/or peripheral devices.
Similarly, the behavior analysis module 1700 can access data from non-sensor sources 1704. For example, the behavior analysis module 1700 can access a local clock and calendar service (e.g., a component of the motherboard 402 or of the processor 502). The behavior analysis module 1700 can use the local clock and/or calendar information to determine, for example, times of day that the user is in the 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. As mentioned, the behavioral algorithm 1702 can include 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.
In the example shown, the behavioral analysis module 1700 and the behavioral algorithm 1702 are shown as components of the pump motherboard 402. However, other configurations are 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 chipset of chips that include separate and multiple analog and digital processors. The processor 1852 can provide, for example, for coordination of the other components of the mobile computing device 1850, such as control of user interfaces, applications run by the mobile computing device 1850, and wireless communication by the mobile computing device 1850.
The processor 1852 can communicate with a user through a control interface 1858 and a display interface 1856 coupled to the display 1854. The display 1854 can be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 1856 can comprise appropriate circuitry for driving the display 1854 to present graphical and other information to a user. The control interface 1858 can receive commands from a user and convert them for submission to the processor 1852. In addition, an external interface 1862 can provide communication with the processor 1852, so as to enable near area communication of the mobile computing device 1850 with other devices. The external interface 1862 can provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces can also be used.
The memory 1864 stores information within the mobile computing device 1850. The memory 1864 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory 1874 can also be provided and connected to the mobile computing device 1850 through an expansion interface 1872, which can include, for example, a SIMM (Single In Line Memory Module) card interface. The expansion memory 1874 can provide extra storage space for the mobile computing device 1850, or can also store applications or other information for the mobile computing device 1850. Specifically, the expansion memory 1874 can include instructions to carry out or supplement the processes described above, and can include secure information also. Thus, for example, the expansion memory 1874 can be provide as a security module for the mobile computing device 1850, and can be programmed with instructions that permit secure use of the mobile computing device 1850. In addition, secure applications can be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
The memory can include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below. In some implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The computer program product can be a computer- or machine-readable medium, such as the memory 1864, the expansion memory 1874, or memory on the processor 1852. In some implementations, the computer program product can be received in a propagated signal, for example, over the transceiver 1868 or the external interface 1862.
The mobile computing device 1850 can communicate wirelessly through the communication interface 1866, which can include digital signal processing circuitry where necessary. The communication interface 1866 can provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service), among others. Such communication can occur, for example, through the transceiver 1868 using a radio-frequency. In addition, short-range communication can occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, a GPS (Global Positioning System) receiver module 1870 can provide additional navigation- and location-related wireless data to the mobile computing device 1850, which can be used as appropriate by applications running on the mobile computing device 1850.
The mobile computing device 1850 can also communicate audibly using an audio codec 1860, which can receive spoken information from a user and convert it to usable digital information. The audio codec 1860 can likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 1850. Such sound can include sound from voice telephone calls, can include recorded sound (e.g., voice messages, music files, etc.) and can also include sound generated by applications operating on the mobile computing device 1850.
The mobile computing device 1850 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a cellular telephone 1880. It can also be implemented as part of a smart-phone 1882, personal digital assistant, or other similar mobile device.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
As shown in
The bed system 1902 can also include a bed controller 1910, a pump 1912, and one or more sensors 1920A-N. The bed controller 1910, as described throughout this disclosure, can be configured to control one or more components and operations of the bed system 1902. The pump 1912 can be configured to receive instructions (e.g., from the bed controller 1910) that, when executed, cause the pump 1912 to inflate or deflate the air chamber(s) of the mattress 1904. The sensors 1920A-N can be positioned throughout the bed system 1902. The sensors 1920A-N can include pressure sensors. The sensors 1920A-N can be positioned around, proximate, and/or inside the air chamber(s) of the mattress 1904 and/or the pump 1912. Therefore, the sensors 1920A-N can detect changes in pressure, which can be used to determine presence of a leak in the bed system 1902. In some implementations, one or more of the sensors 1920A-N can be remote from the bed system 1902. For example, one or more of the sensors 1920A-N can be sensors placed adjacent to the bed system 1902 in the sleep environment 1900.
One or more components of the bed system 1902 may also be in communication with a user device 1914 and/or a remote server 1916 via network(s) 1918. The network(s) 1918 can include local networks in the sleep environment, such as Bluetooth and/or Internet. Data collected by the sensors 1920A-N can be transmitted directly to the remote server 1916 via the network(s) 1918. This collected data can also be transmitted from the sensors 1920A-N to the bed controller 1910. The bed controller 1910 can then communicate the data to the remote server 1916 via the network(s) 1918. One or more other communications between the components described herein is also possible.
The user device 1914 can be any appropriate type of mobile computing device, including but not limited to a smartphone, mobile phone, cellphone, laptop, and/or tablet. The user device 1914 can be used by the technician 1908 who is tasked with setting up and/or initializing the bed system 1902 in the sleep environment 1900. In some implementations, the user device 1914 can also be used by the user of the bed system 1902. For example, the user device 1914 can be a smartphone of the user. The user device 1914 can communicate with the remote server 1916 and/or another computing system to initiate a leak detection test as described herein.
As an illustrative example, the user of the bed system 1902 can call or contact (e.g., via message, via the mobile application presented at the user device 1914, etc.), with their user device 1914, a customer service agent with a concern that their bed system 1902 is leaking. The customer service agent, while remote from the bed system 1902, can initiate the leak detection test using their respective computing device (e.g., the remote server 1916). In some implementations, the user device 1914 can directly initiate the leak detection test instead of going through the remote server 1916 and/or the computing device of the customer service agent.
The remote server 1916 can be any appropriate type of computing system and/or network of computing systems, including but not limited to a computer system, a computer, a network of computers, a network of devices, a cloud-based computer system, and/or a cloud-based service or network of computer systems. The remote server 1916 can perform the leak detection test as described herein. In some implementations, the leak detection test can be performed by the bed controller 1910, on the edge at the bed system 1902.
Still referring to the illustrative example of
In block B, the technician 1908 can generate a signal at their user device 1914 to initiate a leak detection test. The signal can be transmitted via the network(s) 1918 from the user device 1914 to the pump 1912 and/or the bed controller 1910. If the bed controller 1910 receives the signal, the bed controller 1910 can then generate instructions that, when executed, cause the pump 1912 to initiate the leak detection test. In some cases, this leak detection test can be part of a larger program or routine run by the technician 1908 upon completion of installation of the mattress 1904 and the bed system 1902. This program can, for example, log time and location of the installation, receive a user signature attesting to the bed's delivery, generate a receipt, etc.
Initiating the leak detection test can include inflating the air chamber(s) of the mattress 1904 to a highest/fullest amount (e.g., firmness). The air chamber(s) can also be inflated to a predetermined threshold amount of inflation. In some implementations, the air chamber(s) can be inflated to a highest inflation amount for normal operation of the bed system 1902 (e.g., when the user rests on the bed system 1902 or otherwise is on the bed system 1902). In some implementations, the air chamber(s) can be over-inflated to an amount that exceeds the highest/fullest amount of inflation when in use by the user of the bed system 1902 (e.g., if the user can adjust the firmness to a setting of 100, then initiating the leak detection test can include adjusting the firmness to a setting over 100, such as 200). Over-inflating the air chamber(s) of the mattress 1904 can be beneficial to reduce an amount of time during which pressure data is to be collected at the bed system 1902. As a result, a determination of whether a leak is present can be made quickly.
Before the pump 1912 inflates the air chamber(s) of the mattress 1904, the bed controller 1910 can generate instructions to collect data from the sensors 1920A-N about a current condition of the bed system 1902. For example, the data can include pressure data. The pressure data can be used by the bed controller 1910 to determine whether a foundation of the bed system 1902 is in a flat or neutral position. The bed controller 1910 can also use the pressure data to determine whether a user is resting on top of the mattress 1904. If the foundation is flat, the bed controller 1910 can generate instructions that cause the pump 1912 to inflate the mattress 1904 to the highest/fullest amount. If the foundation is not flat, the bed controller 1910 can generate instructions that cause an articulation system of the bed system 1902 (not depicted) to adjust the foundation to the flat or neutral position. If the bed controller 1910 determines that the bed system 1902 is empty (no one is laying on top of the mattress 1904), then the bed controller 1910 can generate the instructions that cause the pump 1912 to inflate the mattress 1904. If the bed controller 1910 determines that the bed system 1902 is not empty, the leak detection test may not be initiated until the bed system 1902 is empty. In some implementations, the bed controller 1910 can still generate instructions that cause the pump 1912 to inflate the mattress 1904, even if the bed system 1902 is not empty (e.g., someone is laying on top of the mattress 1904 or other pressure/weight is detected on top of the mattress 1904, such as a pet or a suitcase).
Once the leak detection test is initiated (block B), the bed system 1902 can collect data for the leak detection test in block C. More particularly, the sensors 1920A-N can detect pressure signals around the pump 1912 and inside the mattress 1904 (e.g., in the air chamber(s)). The detected pressure signals can be transmitted to the bed controller 1910, which can collect this data for a predetermined amount of time. For example, the pressure data can be collected for a length of time that the leak detection test is performed. The leak detection test can be performed for 5 minutes when the bed system 1902 is initially set up. The leak detection test can be performed for one or more other lengths of time, including but not limited to 6 minutes, 8 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, 1 hour, 3 hours, 5 hours, 10 hours, 12 hours, etc. The leak detection test can also be performed for one or more lengths of time that are less than 5 minutes, including but not limited to 4 minutes, 3 minutes, 2 minutes, 1 minute, 30 seconds, etc. For example, when the leak detection test is performed after the bed system 1902 is initially set up, pressure data can be collected for a length of time between sleep sessions of the user (e.g., the user may wake up at 6 AM and go to bed at 9 PM, so between 6 AM and 9 PM, the pressure data can be collected). In the example of
Once the pressure data is collected in block C, the pressure data can be transmitted to the remote server 1916 in block D. The pressure data can be transmitted in batches (e.g., after all of the pressure data is collected for the length of time that the leak detection test is performed). The pressure data can also be transmitted in real-time or near real-time, as the data is collected in block C. The pressure data can be transmitted by the bed controller 1910 to the remote server 1916 in block D. In some implementations, the pressure data can be transmitted by the sensors 1920A-N to the remote server 1916 in block D. In yet some implementations, the pressure data can be transmitted to the bed controller 1910 instead of the remote server 1916. The bed controller 1910 can then be configured to process the pressure data and perform blocks E-F on the edge at the bed system 1902.
The remote server 1916 can apply one or more models to the pressure data to determine whether the bed system 1902 has a leak (block E). As described further below, the model(s) can be trained using machine learning techniques to detect presence of a leak in the bed system 1902. The pressure data can be provided as input to the model(s). The model(s) can output an indication of whether there is presence of a leak in the bed system 1902. In some implementations, the model(s) can also output a confidence value indicating likelihood or confidence of the leak presence in the bed system 1902. The confidence value can be a numeric value on a predetermined scale, such as 0 to 1 and/or 0 to 100. In some implementations, the remote server 1916 can determine the confidence value based on the output from the model(s), the model(s) output being the indication of whether there is a leak in the bed system 1902.
In some implementations, the remote server 1916 may run one or more algorithms and/or rule engines to determine whether a leak is present in the bed system 1902. For example, the remote server 1916 can receive pressure data at time 0, when the bed system 1902 was inflated to the maximum firmness settings and pressure data at a time when the threshold period of time for data collection ends (e.g., at the end of 5 minutes of pressure data collection). The remote server 1916 can calculate a difference in pressure at the 2 times. The difference in pressure can be compared to known or expected pressure values for the bed system 1902 and/or similar bed systems. The difference in pressure can be compared to a threshold pressure change value to determine whether the difference in pressure exceeds the threshold value and thus indicates presence of a leak. The difference in pressure can also be provided as input to a machine learning model that was trained to determine a probability or confidence that the bed system 1902 has a leak based on the difference in pressure.
In some implementations, and as described further below, the remote server 1916 can analyze the pressure data to determine and estimate a size of a hole in the bed system 1902, then use machine learning techniques to correlate the hole size with a leak or no leak. Determining whether the bed system 1902 has a hole that causes a leak can require a variety of additional data, such as a shape of the bed system 1902, a size of the bed system 1902, a type of the bed system 1902, and/or volume of the air chamber(s) of the bed system 1902.
As described further below, the remote server 1916 can also determine whether a leak is present based on determining a leak rate for the bed system 1902. The leak rate can be determined based simply on pressure data collected at the bed system 1902 over the predetermined period of time. After all, a drop in pressure over time can be correlated with leak rate and thus presence of a leak in the bed system 1902. When determining the leak rate of the bed system 1902, the air chamber(s) can be inflated to a maximum firmness or pressure setting (or more than a maximum user-specified firmness setting) and pressure data can be collected during the threshold period of time. A fixed volume can be assumed for the air chamber(s) because the size of the air chamber(s) is known and the air chamber(s) is being filled to the max. Thus, a physics formula for leak rate can be used. For example, the ideal gas law of PV=nRT can be used to derive a feature that correlates with air loss in the bed system 1902. Additionally, given that all air chambers in the bed system 1902 can be inflated to roughly the same firmness setting/pressure, the difference in pressure can be analyzed to determine the leak rate for the bed system 1902. After all, the effect of the material of the air chamber expanding under pressure is similar for all air chambers, but all the air chambers would likely experience a drop in pressure, with the magnitude of the drop in pressure being the differentiator between the air chambers.
The determined hole and/or leak rate can then be classified by the remote server 1916 to determine whether the bed system 1902 has a leak. For example, if the hole size exceeds a threshold size, the bed system 1902 can have a leak. As another example, if the leak rate exceeds a threshold leak rate, the bed system 1902 can have a leak. In some implementations, the remote server 1916 can determine either the hole size or the leak rate to identify presence of a leak. In some implementations, the remote server 1916 can determine both the hole size and the leak rate to improve accuracy in identifying presence of the leak. Both the hole size and leak rate determinations can generate same or similar output values and thus have a linear relationship. Because they are highly correlated, the hole size and leak rate determinations can be used to improve performance of the leak detection test and specifically narrow down a cause of a leak or other issue in the bed system 1902 (e.g., determining the hole size can be beneficial to determine what type of servicing is required, what parts may need to be replaced, and cost of fixing the hole). Both the hole size and leak rate can be determined to validate an identification of a leak in the bed system 1902 (e.g., the remote server 1916 can determine the leak rate, identify a leak based on the leak rate, then determine a hole size to confirm that the bed system 1902 in fact has a leak caused by the hole). In some implementations, both the hole size and leak rate can be determined to generate, test, and validate one or more machine learning models that determine leak presence in bed systems. For example, the models can be trained using hole size and leak rate to learn mathematical relationships between pressure and leakage in order to generate leak values and approximations of leaks in other bed systems. The combination of hole size and leak rate determinations can be used in a variety of other implementations and use cases to improve accuracy and efficiency in detecting leaks in bed systems.
The remote server 1916 can also generate output based on the determination from the model(s) (block F). The output can include an indication of whether the bed system 1902 has a leak. The output can also include one or more suggestions for service actions that can be taken to remedy the leak (if a leak is detected). The output can also include one or more suggestions of what other actions can be taken to diagnose the bed system 1902 in situations where a leak is not detected. For example, suggestions to change surrounding environmental conditions in the environment 1900 can be generated as output to compensate for sudden drops in environmental air pressure and/or temperature, thereby causing a perceived loss of pressure in the bed system 1902.
The output can also include the confidence value determined as output from the model(s) and/or determined by the remote server 1916. In some implementations, depending on the machine learning trained model or other algorithm(s) that is used (e.g., such as a physics-based model or physics-based formula), the output can also include an indication of a size of the hole 1906 in the mattress 1904, a location of the hole 1906 a side of the bed system 1902 where the hole 1906 is located, and/or a leak rate. This information can help the technician 1908 or other relevant user quickly locate the leak and address it.
The output generated in block F can be transmitted to and presented at the user device 1914. In some implementations, the output can also be stored in a data store and/or transmitted to one or more other computing systems, such as a user device of the user of the bed system 1902 and/or a computing device of a customer service representative who can hire or request a technician to fix the bed system 1902 in the sleep environment 1900.
Although
Referring to the process 2000, the bed controller 1910 can initiate a diagnostic test to check for a leak in the bed system (block 2002). As described in
Initiating the diagnostic test in block 2002 can include transmitting a signal to the pump 1912 of the bed system to inflate the mattress of the bed system. Initiating the diagnostic test in block 2002 can therefore include generating instructions, that when executed, cause the pump 1912 to adjust a pressure setting of the bed system (block 2004). The pump 1912 can inflate the mattress of the bed system to a fullest amount of inflation or a highest pressure setting. In some implementations, the pump 1912 can inflate the mattress of the bed system to an amount of inflation that exceeds the fullest amount of inflation or the highest pressure setting that a user can select during operation and use of the bed system. For example, if the user can set the bed to a highest pressure setting of 100/100 during use, the pump 1912 can inflate the mattress to any pressure setting over 100/100 in block 2002. The value over 100/100 that the mattress is inflated to can vary depending on the pump 1912. For example, the mattress can be inflated to a value over the highest pressure setting that the pump 1912 is capable of reaching without causing air chamber(s) of the mattress to burst or experience a seam failure. For example, the mattress can be inflated to a value of 200/100, which can be equivalent to approximately 1.2 psi, without causing any failure with the air chamber(s) of the mattress. In some implementations, the pump 1912 can inflate the mattress to a pressure setting that is double the maximum pressure setting of the bed system during operation and use. The higher the pressure setting that the mattress is adjusted to by the pump 1912 in block 2002, the improved accuracy and ability of the remote server 1916 to detect small leaks in the bed system. Moreover, as described herein, the higher the pressure setting in block 2002, the less time needed to collect and test pressure data.
As described throughout this disclosure, the diagnostic test in block 2002 can be initiated once a current state of the bed system is recorded as initial bed settings. The bed system can then be set to the diagnostic test testing configuration (e.g., highest pressure setting) to run the test. Once the diagnostic test is completed, the bed controller 1910 can generate instructions that cause the pump to return the bed system to the initial bed settings that were recorded before the diagnostic test was initiated.
The sensors 1920A-N can then collect pressure data in block 2006. At least one of the sensors 1920A-N can be in fluid communication with the pump 1912 to collect pressure signals around the pump 1912. At least one of the sensors 1920A-N can also be in fluid communication with at least one air chamber of the mattress of the bed system, as described in
The sensors 1920A-N can include pressure sensors that sense pressure on the mattress of the bed system. The pressure sensors can be exposed to pressure fluctuations of the bed system. The pressure sensors can already be part of the bed system and can be used for multiple purposes. For example, the pressure sensors can be used to detect biometric data about users who sleep or lay on the bed system. Therefore, the disclosed techniques can be performed without requiring additional hardware to be purchased and configured/attached to the bed system. Pressure fluctuations that are detected by the pressure sensors can be the result of not just weight of a user pressing on the bed system, but also movements and vibrations of the user and other elements of the environment (e.g., cardiac motion, respiratory motion, gross motor motion, acoustic vibrations, etc.). The sensors 1920A-N can generate, based on the pressure fluctuations, pressure readings recorded in digital signals. In some cases, as described herein, the sensors 1920A-N can sense pressure changes to one or more air chambers in the mattress. In some cases, the sensors 1920A-N can sense pressure transmitted through one or more legs of a frame/foundation of the bed system. Moreover, in some implementations, the sensors 1920A-N can include a strip, pad, or mat placed in or about a mattress, including a mattress with no air bladder.
The pressure data can be collected for one or more lengths of time or predetermined amounts of time. The lengths of time or predetermined amount of time can be a sleep session of the user, an amount of time between consecutive sleep sessions of the user (e.g., 2 sleep sessions, 3 sleep sessions, 5 sleep sessions, etc.), one or more sleep sessions of the user, 5 minutes, and/or 24 hours. One or more other lengths of time or predetermined amounts of time are also possible. For example, during an initial set up of the bed system, the sensors 1920A-N can collect the pressure data for a short period of time, such as 5 minutes. The shorter period of time can be beneficial to detect fast and/or large leaks in the bed system. As another example, after the bed system is set up, the sensors 1920A-N can collect the pressure data for a longer period of time, such as 1 hour, hours, 10 hours, 12 hours, etc. The longer period of time can be beneficial to detect slow and/or small leaks in the bed system. The longer period of time for pressure data collection can occur between sleep sessions, when the user is not in the bed system. In yet some implementations, the predetermined amount of time can vary based on the pressure setting that the pump 1912 adjusts the bed to in block 2004. For example, the higher the pressure setting, the less amount of time may be needed to collect the pressure data. If the mattress of the bed system is set to the maximum user-specified pressure setting of 100/100, for example, then the pressure data can be collected in block 2006 for 5 minutes. As another example, if the bed system is set to double the maximum user-specified pressure setting, such as 200/100, then the pressure data can be collected for 2.5 minutes. One or more other variations in time for collecting the pressure data can also be realized.
The remote server 1916 can receive the pressure data in block 2008 for the predetermined amount of time. The remote server 1916 can then provide the pressure data as input to a leak detection model in 2010. The leak detection model was trained using machine learning techniques to detect presence of a leak in a mattress of the bed system based at least in part on the received pressure data. In some implementations, the leak detection model (such as a physics-based model) can be trained using machine learning techniques to detect the presence of a leak in a mattress based on training data that includes estimated sizes of holes in mattresses. The estimated sizes of holes in the mattresses can be determined based on deltas in pressure changes in the mattresses, as detected and labeled during training of the model. Moreover, in some implementations, the leak detection model can be trained using machine learning techniques to detect a side of the mattress having the leak. The detection of the side of the mattress having the leak can be based on a gas leak rate equation.
The leak detection model can also be referred to as a leak detection classifier, as described in
In some implementations, leak detection models can be trained to detect leaks in different types and/or sizes of mattresses. Accordingly, in block 2010, the remote server 1916 can optionally determine a type of the mattress based on information about the bed system, select a model that was trained, using machine learning techniques, to detect a leak in mattresses of the same type as the mattress, and then provide, as input, the received pressure data to the selected model. The information about the bed system can be retrieved from a data store. The information about the bed system can also be received from a user device, such as the user device of a technician who is setting up the bed system and/or a device of a user of the bed system after the bed system is already set up.
The remote server 1916 can receive output from the leak detection model indicating whether a leak is detected (block 2012). Thus, the model can output data indicating the detected presence of a leak in the mattress of the bed system. In the example of the leak detection classifier, the remote server 1916 can receive a leak detection classification from the leak detection classifier. This classification can take a variety of formats. For example, the classification may be a Boolean value (e.g., Leak/No Leak). In some cases, the classification may include continuous values, such as numbers (e.g., to indicate an intensity or confidence level that a leak is detected).
In some implementations, the remote server 1916 can generate an aggregated value for a detected leak from a plurality of leak detection classifications for a particular amount of time. For example, the remote server 1916 can compile various classification values for periods of time between several consecutive sleep sessions of the user. The remote server 1916 can therefore monitor the bed system over a longer period of time, such as a day, week, month, etc. to determine whether the bed system has a slow leak. With this single canonical timeline, the remote server 1916 can generate an overall value for the detected leak. The overall value can indicate a higher (or lower) confidence that the leak is actually detected in the bed system. By using non-contiguous periods of time between sleep sessions, the remote server 1916 can create an aggregated value that is less sensitive to random and/or non-repeating events that may impact accurate detection of a leak in the bed system.
The remote server 1916 can generate a confidence value based on the model output in block 2014. The confidence value can be a leak confidence value that can indicate a likelihood that a leak is present in the bed system. A higher confidence value can indicate a higher likelihood that the leak is present and thus servicing is required to fix/resolve the leak issue. A lower confidence value can indicate a lower likelihood that the leak is present. In some implementations, the confidence value can also be generated by the leak detection model and returned as model output.
The remote server 1916 can generate one or more service actions based on the model output and/or the confidence value in block 2016. The remote server 1916 may generate the service action(s) regardless of the confidence value (therefore, if a leak is detected, a service action is generated). In some implementations, the remote server 1916 may generate the service action(s) if the confidence value is within a threshold range and/or exceeds a threshold confidence value. Thus, the service action(s) may be generated if the bed system has a large enough leak that causes the bed system to not function properly (e.g., air chambers of the bed system do not retain air and constantly keeps deflating, regardless of whether a user or other object rests on top of the bed system). In some implementations, the service action(s) may be generated if the speed at which air leaks from the bed system exceeds a threshold range or value. In yet some implementations, the service action(s) may be generated if an estimated hole size in the bed system exceeds a threshold size.
Service actions that are generated by the remote server 1916 can be transmitted directly to a user device of a technician or other customer support representative (e.g., refer to the technician 1908 and the user device 1914 in
The remote server 1916 can generate service actions that include ordering one or more components (e.g., air chambers, pumps, mattress, sensors, etc.) to fix the leak in the mattress of the bed system. The remote server 1916 can also generate service actions that include scheduling one or more technicians to fix the leak in the mattress. One or more other service actions can also be generated and based on the data indicating the presence of the leak. For example, if a leak is not detected, the service actions can include testing one or more other components of the bed system to identify a possible source of the issue. If a leak is not detected, the service actions can include suggestions to adjust or modify surrounding environmental conditions in a sleep environment, which may cause perceived pressure loss in the bed system. For example, the suggestions can include adjusting an air temperature and/or air pressure in the sleep environment to mitigate drops in environmental temperature and/or pressure (which may be caused by sudden storms or other environmental conditions).
Referring to the process 2100, the computer system can receive an indication to run a leak detection test for a bed system in block 2102. Refer to block B in
Before pressure settings of the bed system are adjusted to test for a leak in the bed system, the computer system can determine whether the bed system is empty (block 2104). The computer system can receive initial pressure data sensed by at least one sensor of the bed system. Using the initial pressure data, the computer system can determine whether a user is laying on a mattress of the bed system. The computer system can also determine whether any other object is resting on top of the mattress of the bed system. If the bed system is inflated, then the pressure setting of the bed system can be adjusted to test for leakage. This operation can advantageously ensure that the bed is being subject to pressure from the ambient environment and bedding, which can be expected to be either constant or within a small range compared to the pressure applied by one or more users laying on the bed. By ensuring that no user is on the bed, and by using models trained with the assumption that no user is on the bed, accuracy can be improved over alternatives that do not determine if a user is on the bed.
In block 2106, the computer system can also determine whether the bed system is flat. The computer system can receive the initial pressure data and use that data to determine whether a foundation of the bed system is flat or in a neutral position. The computer system can also use other data, such as indications from a user device configured to control components, such as the foundation, of the bed system and/or a controller of the bed system to determine whether the foundation is in any other position other than the flat or neutral position. If the foundation is flat, then the pressure setting of the bed system can be adjusted to test for leakage.
It can be noted that blocks 2104 and 2106 can be performed simultaneously or in any other order. In some implementations, only one of the blocks 2104 and 2106 may be performed by the computer system. Moreover, if the computer system determines that the bed system is not empty, the pressure setting of the bed system may not be adjusted and the leak detection test may not be performed until the computer system determines that the bed system is empty. Therefore, the computer system can continuously receive pressure data from at least one sensor of the bed system and determine, based on the pressure data, when and if the bed system is empty. Once the bed system is determined to be empty, the computer system can proceed to block 2108 described below. In some implementations, the computer system can determine whether the bed is empty based on prompting the user/sleeper of the bed system to confirm that the bed is in fact empty (e.g., by presenting a notification or message in a mobile application at a user device of the user).
If the computer system determines that the bed system is not flat, the computer system can control the bed system to adjust the foundation to the flat or neutral position. The computer system can, for example, generate instructions that cause an articulation system of the bed system to adjust or move the foundation to the flat or neutral position. Once the foundation is adjusted to the flat or neutral position, the computer system can poll the at least one sensor to collect pressure data. In some implementations, the computer system may not poll the at least one sensor for data until the mattress of the bed system is inflated to a fullest amount.
In some implementations (not depicted in the process 2100), the computer system may also determine whether any heating or cooling features (e.g., core heating/cooling, foot warming) are activated at the bed system. If any of these features are activated, the computer system can generate instructions that cause a heating/cooling unit of the bed system to deactivate the feature(s). Once the feature(s) is deactivated, the computer system can proceed with steps of the diagnostic test described herein. Sometimes, information detected about the effects of the activated heating/cooling features on pressure of the bed system can be recorded and used by the computer system to adjust and/or train one or more of the models described herein. Accordingly, the models can be trained to detect effects of heating or cooling features on leak aspects of a bed system, such as leak detection rates and/or estimated hole sizes.
Next, the computer system can transmit instructions to a pump of the bed system that cause the pump to inflate the mattress of the bed system to a fullest amount. Therefore, these instructions can be transmitted once the computer system determines that the bed system is flat and/or empty (block 2108). Refer to block 2004 in
The computer system can receive detected data from sensors of the bed system for a predetermined amount of time in block 2110. The computer system can, for example, poll at least one sensor of the bed system for pressure data. The computer system can poll the at least one sensor based on a determination that the foundation is flat and/or that the bed system is empty (e.g., the user is not laying on the mattress of the bed system). Additionally or alternatively, the computer system can poll the at least one sensor based on a determination that the pump has inflated the mattress to the fullest amount. This determination can be based on receiving indication from the pump that the pump has completed full inflation of the mattress. This determination can also be based on receiving initial pressure data from the at least one sensor indicating that the mattress is at a highest pressure setting and/or no more air is being pumped into the mattress by the pump. Refer to block C in
In block 2112, the computer system can process the received data. The computer system can process the data in real-time, as the data is received from the at least one sensor. The computer system can also process the data in batches, after all or portions of the data is received by the computer system. Processing the received pressure data can include discarding a portion of the data that corresponds to a predetermined amount of time at which the at least one sensor begins to collect the pressure data. The predetermined amount of time can include, but is not limited to, 1 second, 3 seconds, 5 seconds, 8 seconds, 10 seconds, etc. During the predetermined amount of time, the pump of the bed system can be inflating the mattress to the fullest amount. Therefore, pressure data collected while the pump is inflating the mattress can be discarded to ensure that leak detection is accurate and not skewed by rapid changes in pressure that may occur during mattress inflation. In some implementations, during the predetermined amount of time, a valve of the bed system can be opened to release air from within the mattress. Thus, it can be preferred to discard data collected during this time to ensure that any leaks are accurately detected using the disclosed techniques. Activating the pump and/or opening the valve can cause vibrations throughout the bed system, which can cause normal and temporary pressure changes in the mattress. These pressure changes may not account for actual leaks in the bed system and thus are discarded during the processing in block 2112.
The computer system can then return the processed data to be provided as input to a leak detection model (block 2114). Refer to block E in
Referring to the process 2200, the data source 2202 can provide training data in block 2208, which, in block 2210, the classifier factory 2204 can receive. The data provided and received can include various types of data that are useful for creating leak-detection classifiers (e.g., machine learning trained algorithms). For example, the data can include pressure data recording different levels of pressure in bed systems that can indicate different types of leaks. The pressure data can also include data about other aspects of the bed systems, such as pump settings, sizes of holes in the bed systems, and/or rates at which air leaks out of the bed systems. This data can include correspondences between various bed parameters (e.g., air-bladder pressure, temperature) and a classification of the bed (e.g., with a hole versus without a hole, elevated head section versus flat). As will be understood, the model to be trained is a logical construct that uses these real correspondences to predict what such a correspondence would be given new bed parameter values that were not part of the training data.
In some implementations, the training data can include temperature data collected from temperature sensors of bed systems. The temperature data can be collected of air inside air chambers of bed systems. The temperature data can be used alone and/or in combination with other training data described herein to improve accuracy of models that detect leaks in bed systems. The data received by the classifier factory 2204 can also include tagging data that defines tags of leak states for the pressure data. For example, ranges of timestamps in a detected leak can be identified as showing a pressure of a bed system before any leak, a beginning or start of the leak, a greatest amount of leakage in the bed system, and/or a consistent leakage in the bed system. Moreover, the particular data format of the pressure data and tagging data can vary depending on the capabilities of the data source 2202 and/or the classifier factory 2204. For example, the data may take the form of an array indexed by time, with each cell holding one or more pressure values for that time period and another array holding a tag in the same indexing scheme. Other formats are also possible.
Next, the classifier factory 2204 can generate one or more classifiers based on (e.g., using) the training data (block 2212). In some implementations, this generation by the classifier factory 2204 can include training a convolutional neural network (CNN) configured to use as input i) the pressure data and ii) the tagging data. The CNN can also be configured to generate intermediate data for use by later elements of a classifier. In general, the CNN can be configured to perform feature extraction on the pressure data, and as such, the intermediate data can include extracted features of the pressure data. This data can take the form of, for example, numerical data stored on disk in binary format. As will be appreciated, this intermediate data can often be in a form that is incomprehensible to outside observers. However, in some cases, some information can be understood from the pressure data, such as spectral types of information.
Although training is described in reference to training with the CNN, various other machine learning techniques can be used to generate the one or more classifiers. For example, linear models, logistic regression models, SVMs, physics-based models, and one or more other types of machine learning techniques may be used.
In order to perform training using the CNN, various epochs of time in the training data can be identified and extracted. The training can also include training a recurrent neural network (RNN) configured to use as input the intermediate data. That is to say, the output of the CNN can be used as input to the RNN, and the RNN can be configured to generate a leak classification from the extracted features. As will be understood, the RNN may be more complex than just a single RNN. In some examples, the RNN can include multiple layers working in parallel. These layers can include i) a prospective long short-term memory (LSTM) network using as input later leak classifications and ii) a historic LSTM network using as input previous leak classifications. This can allow, for any point in the epoch, classification that takes into account classifications preceding and following that moment of the epoch. In addition, the RNN can be configured to use post-processing functions to generate the leak classification. The particular post-processing operations needed can vary depending on type and format of data used, computing hardware capabilities available, etc. One example post-processing can include concatenating output from a plurality of output nodes of the RNN into a single value or a group of values.
The classifier factory 2204 can transmit the classifier(s) in block 2214, which can be received by the computing device 2206 in block 2216. The computing device 2206 can then use the classifier(s) during runtime to determine presence of leaks in bed systems. As an illustrative example, when the computing device 2206 is manufactured (e.g., the bed controller 1910), the classifier(s) can be loaded into firmware of the computing device 2206. The computing device 2206 can then execute the classifier(s) during runtime in order to accurately detect leaks in the bed system. As another example, when an application is installed on the computing device 2206 (e.g., a mobile phone or home computer), the application can include or download the classifier(s) for runtime use. As yet another example, the computing device 2206 can be remote from bed systems (e.g., the remote server 1916) and the classifier(s) can be loaded into the firmware of the computing device 2206. The computing device 2206 can then execute the classifier(s) during runtime in order to accurately detect leaks in various different bed systems.
The remote server 1916 can execute a linear leak detection model 2300, a polynomial leak detection model 2302, a physics-based leak detection model 2304, a neural network leak detection model 2306, a leak hold detection model 2308, and/or a leak rate detection model 2310. One or more additional, fewer, or other models (e.g., classifiers) can also be trained using other machine learning techniques and executed by the remote server 1916.
The linear leak detection model 2300 can be simple to implement and have high accuracy in detecting leaks. The model 2300 can be a linear regression model, which can model relationships between a scalar response and one or more dependent and/or independent variables. Linear predictor functions can be used and unknown model parameters can be estimated from the data that is provided as input to the model.
The polynomial leak detection model 2302 can be used to determine which input factors (e.g., pressure data) drive responses (e.g., leaks) and in what direction. The model 2302 can include a logistic regression model. The logistic regression model can be used to model probability of a certain class or event existing, such as leak/no leak. The model 2302 can also include a support vector machine (SVM) model. The SVM model can be a supervised learning model with associated learning algorithms that analyze data for classification and regression analysis.
The physics-based detection leak model 2304 can be trained to estimate a size of a hole in a mattress that causes a leak. In other words, the model 2304 can be trained to determine a hole diameter value of the leak in the mattress based at least in part on volume data and pressure data of a bed system. Therefore, the model 2304 can include variables for volume data and pressure data of a bed system. Accordingly, the model 2304 can use governing laws of nature to define physical processes that may occur, such as leakage of a gas from the mattress.
In some implementations, the model 2304 can estimate how large the hole is based on significance of a delta in the pressure data. The model 2304 can also be trained to determine a side of the mattress that has the leak. The model 2304 can use a gas leakage equation to determine the side of the mattress. In some implementations, the gas leakage equation can be: Q=v*(Δp/Δt). One or more other equations can also be used to quantify gas leakage in the mattress and determine the side of the mattress having the leak. Output from the model 2304, such as determined or estimated hole sizes, can be used for training other models (e.g., classifiers) described herein, such as a logistic regression model and/or the SVM model.
The neural network leak detection model 2306 can be used to analyze pressure data over long periods of time to automatically determine leaks in bed systems. The model 2306 can therefore be used to accurately detect slow and/or small leaks in bed systems that may not be as easily detected during a short period of data collection. In some implementations, the model 2306 can be a CNN. Refer to
The leak hole detection model 2308 can be trained to perform one or more of the techniques described herein. For example, the model 2308 can be trained to detect presence of a hole in an air chamber. The model 2308 can also be trained to estimate a size of the hole. In some implementations, the model 2308 can also be trained to identify a location and/or side of the bed system having the hole. The model 2308 can be trained with physics-based formulas to accurately identify and predict the size of the hole. The model 2308 can then be trained to classify the size of the hole with an indication of leak presence or no leak presence.
The leak rate detection model 2310 can be trained to perform one or more of the techniques described herein. For example, the model 2310 can be trained to correlate changes in pressure over time with indications of leak presence or no leak presence.
In some implementations, a pressure drop model using any of the abovementioned machine learning techniques can be used during bed setup and/or during every day/daily use of the bed system. Additionally or alternatively, a model that is trained to estimate a hole size can be used during bed setup and/or during every day/daily use of the bed system. Although some models are depicted and described throughout this disclosure, one or more other machine learning techniques, algorithms, and/or models may be used to perform the disclosed technology.
Referring to the process 2400, the computer system can receive an indication to run a leak detection test for a bed system (block 2402). The indication can be received from a user device of a technician, customer service agent, and/or user of the bed system. In some implementations, for example, the user of the bed system can call a customer service agent to test the bed system for a potential leak. The customer service agent can select an option presented in a mobile application or user interface at their respective computing device to initiate a leak detection test at the bed system, even though the customer service agent is remote from a physical location of the bed system. When the customer service agent initiates the test, communication can be established between the computing device of the customer service agent and the controller of the bed system, for example, such that the leak detection test can be performed as described throughout this disclosure. Refer to block 2102 in the process 2100 of
The computer system can determine whether the bed system is empty in block 2404. Refer to block 2104 in the process 2100 of
The computer system can also determine whether the bed system is flat in block 2406. Refer to block 2106 in the process 2100 of
In block 2408, the computer system can transmit instructions to (i) a pump to inflate a mattress of the bed system to a threshold amount and (ii) a controller of the bed system to adjust the bed to leak detection testing settings. The instructions can be transmitted automatically in response to determining that the bed system is at least empty. In some implementations, the instructions can also include instructing components of the bed system, such as the controller, to first adjust the bed system to a flat position before performing (i) and/or (ii). In block 2408, the pump can receive instructions to inflate the mattress to a maximum pressure setting. The pump can also receive instructions to inflate the mattress to a pressure setting that exceeds the maximum pressure settings, as described herein. As an illustrative example, the pump can receive instructions to inflate the mattress to double the maximum pressure setting for the bed system, as described herein.
The controller can also receive instructions to adjust the bed to the leak detection testing settings. In some implementations, the computer system can be the controller. Therefore, the computer system can simply execute the instructions in block 2408. The leak detection testing settings can include adjusting the bed system to a flat position when the bed is empty. Adjusting the bed to the leak detection testing settings can include adjusting the pressure settings of the mattress as described above. In some implementations, the leak detection test can be performed to test one air chamber at a time inside the mattress. Therefore, the computer system can selectively adjust one air chamber at a time (e.g., an air chamber on one side of the bed) inside the mattress in block 2408. The pressure can then be analyzed for that particular air chamber and then the process 2400 can be repeated for each other air chamber in the mattress. In some implementations, the computer system can selectively adjust any combination of air chambers (e.g., all air chambers on one side of the bed, air chambers near a head end of the mattress, air chambers near a foot end of the mattress, etc.) inside the mattress in block 2408 and then test the combination of air chambers for presence of a leak. In some implementations, the computer system can adjust all the air chambers inside the mattress in block 2408 to test all of the air chambers at once. Refer to block 2108 in the process 2100 of
Moreover, in some implementations, the computer system can store or save current settings of the bed system before adjusting the bed to the leak detection testing settings. The current settings can be stored in temporary storage, local memory, random access memory (RAM), flash, and/or non-volatile memory of the computer system for quick retrieval at a future time. For example, the current settings can be retrieved after the leak detection test is run. The current settings can then be implemented by the computer system or the controller to return the bed system to the settings before the bed was adjusted to the leak detection testing settings (refer to block 2420). The current settings can include a current positon of the bed system (e.g., an elevated head portion and/or foot portion of a foundation of the bed system), a responsive air functionality of the bed system (e.g., activation of a heating and/or cooling routine), and/or a firmness or pressure setting of the bed system. Any of these current settings and/or all of the current settings can be saved and therefore retrieved after the leak detection testing so that the computer system can adjust (or revert) the bed system back to the settings before the leak detection testing was performed.
In block 2410, the computer system can collect pressure data from the bed system for a threshold amount of time. Refer to block 2110 in the process 2100 of
The computer system can determine a leak rate based on the collected pressure data (block 2412). The leak rate formula used by the computer system can assume that volume inside the air chamber(s) of the mattress remains constant. The leak rate can be determined as a change in pressure over time that the pressure data is collected. In some implementations, as described herein, the computer system can run an algorithm and/or one or more rules to determine the leak rate for the bed system. Sometimes, the computer system can run a machine learning trained model to determine the leak rate for the bed system.
In some cases, a computer function stored on disk may include one or more computational tests encoded in computer-executable instructions to identify a leak rate. This computational test can be devised and encoded based on permutations an ideal gas law of PV=nRT to derive a feature, calculated by computing hardware based on sensor input, that correlates air loss with leak rate. For a leak, a mole of gas loss from a test volume after t seconds can be calculated as:
Where LR is the leak rate. The moles of air remaining in the volume at time t can be:
Assuming a constant temperature, the pressure at time t can be:
In some implementations, temperature may change during a leak detection test. For example, foot warming and/or core heating functions can be in use at the bed system before performing a leak detection test. Either of these functions can cause a change in temperature of the air in the bed system. As another example, during a sleep session, a body temperature of a sleeper can cause an increase in temperature (and/or pressure) of the air in the bed system. The abovementioned changes in temperature can also be accounted for in formulas used to determine the leak rate for the bed system.
And, a change in pressure can be:
Thus, the leak rate can be a function of volume of the air chamber:
In some implementations, moles of air lost as a portion of the starting amount can also be considered for determining the leak rate:
In some implementations, one or more other physical models may be used, such as a model that takes into account deformation of the air chamber by considering elastic properties of the air chamber.
Moreover, since pressure must be an absolute value, the standard atmospheric pressure of 101,325 Pa is added to the gauge pressure measurements obtained from the pressure sensors described herein. Besides using standard atmospheric pressure, the bed system may also include a pressure sensor that measures current atmospheric pressure. As another example, the bed system can include a pressure sensor that measures absolute air pressure inside the air chamber (instead of or in addition to a gauge pressure sensor of the bed system).
The computer system can determine whether the leak rate exceeds a threshold leak rate value in block 2414. The threshold leak rate value can, in some implementations, be the same for any bed system, bed type, air chamber size, and/or quantity of air chambers in the bed system. The same threshold leak rate value can be used because, in determining the leak rate, the volume of the air chambers is assumed to be constant when the air chambers are filled to the maximum pressure setting (or a pressure setting that exceeds the maximum pressure setting). In some implementations, the threshold leak rate value can be determined using a machine learning model. The machine learning model can be trained to determine the threshold indicative of a leak in a bed system based on known, collected, and/or labeled/annotated data about various pressure levels and leaks in bed systems. In some implementations, known leak rates can be mapped or graphed to known holes in bed systems to define the threshold leak rate value and thus identify what levels of leak rates are associated with actual leaks in bed systems. In some implementations, the threshold leak rate value can vary depending on the type of bed, air chamber size, quantity of air chambers in the bed system, and/or other characteristics/qualities of the bed system, a geographic or environmental location of the bed system, and/or user demographics.
Although block 2414 is described in reference to determining whether the leak rate exceeds the threshold leak rate value, the same concept can apply to determining whether a pressure drop exceeds a threshold pressure drop value. The same concept may similarly apply to determining whether an estimate of a hole size exceeds a threshold hole size value.
If the leak rate exceeds the threshold leak rate, the computer system can identify a leak in the bed system (block 2416). The computer system can then proceed to block 2420, described below. The computer system can generate in indication of a leak. The indication can be a string value, float value, integer value, and/or Boolean value indicating the leak. In some implementations, the indication of the leak can be a numeric value indicating a confidence, likelihood, or probability that the leak is detected at the bed system.
If the leak rate does not exceed the threshold leak rate, the computer system can identify that there is no leak in the bed system (block 2418), then proceed to block 2420. As described above, the computer system can generate an indication such as a string value, float value, integer value, and/or Boolean value indicating that no leak was detected. In some implementations, the computer system may only generate an indication if a leak is presently detected. In some implementations, when the computer system generates an indication that no leak is present, the computer system can also generate one or more suggestions of actions that can be taken to further diagnose the bed system. For example, the computer system can generate suggestions to check or reset one or more other settings of the bed system, turn on/off a heating/cooling unit of the bed system, and/or check/adjust heating/cooling/humidifiers/etc. in an ambient environment surrounding the bed system. In some implementations, when the computer system generates an indication that no leak is present, the computer system can also generate one or more computer-readable instructions to a device controller to perform diagnostic operations using, for example, a heating/cooling unit of the bed system and/or a device that influences the ambient environment surrounding the bed such as a heating/cooling/humidifying device.
In block 2420, the computer system can return the leak indication from either block 2416 or block 2418, and also re-adjust the bed to the pre-testing settings. As described in reference to block 2114 in the process 2100 of
In some implementations, as described herein, one or more of the blocks of the process 2400 can be performed by a remote server as well as a controller on the edge at the bed system. For example, the remote server can initiate the leak detection test in block 2402. The controller at the bed system can perform blocks 2404-2418 to determine whether a leak is present at the bed system. The controller can then return or transmit the leak determination to the remote server in block 2420. In some implementations, the controller can perform blocks such as 2404-2412, then transmit the determined leak rate to the remote server. The remote server can then perform blocks 2414-2420 to determine whether a leak is likely present at the bed system. One or more other combinations of blocks can be performed by the remote server and/or the controller as described throughout this disclosure.
Referring to the process 2500 in
The bed controller 1910 can receive the instructions in block 2506. Receiving the instructions can indicate that communication has been established with the user device 2502.
In block 2508, the bed controller 1910 can save current bed settings of the bed system. Refer to description of block 2408 in the process 2400 of
The bed controller 1910 can then set the bed system to leak testing settings in block 2510. Refer to block 2408 in the process 2400 of
The sensors 1920A-N can then begin collecting pressure data in block 2512. Refer to block 2410 in the process 2400 of
The bed controller 1910 can receive the pressure data in block 2514. Refer to block 2008 in the process 2000 of
The bed controller 1910 can process the pressure data to determine presence of a leak in block 2516. Processing the pressure data can include determining a leak rate based on the collected pressure data. Refer to the block 2412 in the process 2400 of
The bed controller 1910 can generate a leak presence indication based on processing the pressure data (block 2518). For example, the bed controller 1910 can determine whether a determined leak rate exceeds a threshold leak rate value, which can indicate presence of the leak in the bed system. Refer to blocks 2414-2418 in the process 2400 of
In block 2520, the user device 2502 can receive the indication from the bed controller 1910. The user device 2502 can also receive the actions to be taken in response to the leak presence indication. The user device 2502 can output or present in a graphical user interface display (GUI) at the user device 2502 (i) the indication and/or (ii) one or more of the actions to be taken (block 2522).
Referring back to block 2518, once the bed controller 1910 generates the leak presence indication, in block 2524 the bed controller 1910 can also adjust the bed system back to the bed settings that were saved in block 2508. For example, the bed controller 1910 can return the bed system to ambient pressure settings that were used for or otherwise detected at the bed system before the leak detection test was performed. In some implementations, the bed controller 1910 may return the bed system to user-desired or user-set pressure settings after the leak detection test is performed. Refer to block 2420 in the process 2400 of
The foregoing detailed description and some embodiments have been given for clarity of understanding only. No unnecessary limitations are to be understood therefrom. It will be apparent to those skilled in the art that many changes can be made in the embodiments described without departing from the scope of the invention. For example, a different order and type of operations may be used to generate classifiers. Additionally, a bed system may aggregate output from classifiers in different ways. Thus, the scope of the present invention should not be limited to the exact details and structures described herein, but rather by the structures described by the language of the claims, and the equivalents of those structures. Any feature or characteristic described with respect to any of the above embodiments can be incorporated individually or in combination with any other feature or characteristic, and are presented in the above order and combinations for clarity only.
A number of embodiments of the inventions have been described. Nevertheless, it will be understood that various modifications can be made without departing from the spirit and scope of the invention. For example, in some embodiments the bed need not include adjustable air chambers. Moreover, in some embodiments various components of the foundation 600 can be shaped differently than as illustrated. Additionally, different aspects of the different embodiments of foundations, mattresses, and other bed system components described above can be combined while other aspects as suitable for the application. Accordingly, other embodiments are within the scope of the following claims.
Claims
1. A system comprising:
- a bed system having a mattress to support a user laying on the bed system;
- at least one sensor configured to collect pressure data on the bed system; and
- a computer system comprising a processor and memory, wherein the computer system is configured to: receive the pressure data that is collected by the at least one sensor; provide, as input, the pressure data to a model to detect presence of a leak in the mattress of the bed system based at least in part on the pressure data; receive, as output from the model, data indicating the detected presence of a leak in the mattress; and return a message indicating the detected presence of a leak in the mattress.
2. The system of claim 1, wherein the model is a linear model.
3. The system of claim 1, wherein the model is a polynomial model including at least one of a logistic regression model and a support vector machine (SVM) model.
4. The system of claim 1, wherein the model is a physics-based model that includes variables for volume data and the pressure data of the bed system, wherein the physics-based model is configured to determine a hole diameter value of the leak in the mattress based at least in part on the volume data and the pressure data.
5. The system of claim 1, wherein the model is a neural network model.
6. The system of claim 1, wherein the computer system is configured to receive the pressure data in response to:
- receiving an indication from a user device to initiate a leak detection test for the bed; and
- transmitting a signal to a pump of the bed system to inflate the mattress of the bed system.
7. The system of claim 1, wherein the bed system further comprises a pump configured to inflate and deflate the mattress, wherein the at least one sensor is in fluid communication with the pump.
8. The system of claim 1, wherein the mattress includes at least one air chamber, wherein the at least one sensor is a pressure sensor in fluid communication with the air chamber.
9. The system of claim 1, wherein the bed system further comprises means for controlling pressure of the mattress that includes the at least one sensor.
10. The system of claim 1, wherein the computer system is configured to:
- determine, based on initial pressure data sensed by the at least one sensor, whether a foundation of the bed system is flat;
- determine, based on the initial pressure data sensed by the at least one sensor, whether the user is laying on the mattress; and
- poll, based on a determination that the foundation is flat and the user is not laying on the mattress, the at least one sensor for the pressure data.
11. The system of claim 10, wherein the computer system is configured to:
- control the bed system to adjust the foundation to a flat position based on a determination that the foundation is not flat; and
- poll, in response to adjusting the foundation to the flat position, the at least one sensor for the pressure data.
12. The system of claim 11, wherein the computer system is further configured to store, in local memory of the computer system, current settings of the bed system before controlling the bed system to adjust the foundation to the flat position, wherein the current settings correspond to a state of the bed system.
13. The system of claim 12, wherein the state of the bed system includes at least one of (i) a position of the foundation, (ii) a firmness setting of the mattress, (iii) a responsive air setting for the bed system, and (iv) an activation of a heating or cooling feature of the bed system.
14. The system of claim 12, wherein the computer system is further configured to:
- retrieve, from the local memory and after returning the detected presence of the leak in the mattress, the stored settings of the bed system; and
- control the bed system to adjust to the stored settings.
15. The system of claim 1, wherein the computer system is configured to:
- determine, based on the pressure data, a leak rate for the mattress of the bed system;
- determine whether the leak rate exceeds a threshold leak rate value;
- generate a leak presence indication for the bed system based on a determination that the leak rate exceeds the threshold leak rate value; and
- return the leak presence indication.
16. A system comprising:
- a bed system having a mattress to support a user laying on the bed system;
- at least one sensor configured to collect pressure data on the bed system; and
- a computer system comprising a processor and memory, wherein the computer system is configured to: receive the pressure data that is collected by the at least one sensor; determine, based on the pressure data, a leak rate for the bed system; determine whether the leak rate exceeds a threshold leak rate value; generate a leak presence indication for the bed system based on a determination that the leak rate exceeds the threshold leak rate value; and return the leak presence indication for presentation in a graphical user interface (GUI) display at a user device.
17. The system of claim 16, wherein determining, based on the pressure data, the leak rate for the bed system comprises determining a change in the pressure data over a threshold amount of time that the pressure data is collected by the at least one sensor.
18. The system of claim 16, wherein the user device is a mobile computing device of the user of the bed system.
19. The system of claim 16, wherein the user device is a mobile computing device of a technician setting up the bed system.
20. The system of claim 16, wherein the user device is a computing device of a customer service agent who was requested, by the user of the bed system, to initiate a leak detection test at the bed system.
Type: Application
Filed: Jul 20, 2023
Publication Date: Feb 8, 2024
Inventors: Kevin Blomseth (San Jose, CA), Cristina Marie Jocson (San Francisco, CA), Cesar C. Palerm (Campbell, CA), Yehor Shcherbakov (Kyiv)
Application Number: 18/224,365
