BED HAVING MACHINE LEARNING SLEEP STAGE DETECTING FEATURE

1 - 20 . (canceled)

21 . A system comprising:

a controller configured to be in data communication with one or more sensors of a bed system, the controller configured to:

retrieve one or more sleep stage classifiers, wherein the one or more sleep stage classifiers were trained, using machine learning techniques and based on training data, to generate a respective sleep stage vote when run by the controller on sensor readings;

receive sensor readings from the one or more sensors;

run the one or more sleep stage classifiers on the received sensor readings in order to collect one or more sleep stage votes from the running sleep stage classifiers;

determine, from the one or more sleep stage votes, a sleep stage of a user on the bed system; and

operate the bed system according to the determined sleep stage.

22 . The system of claim 21 , wherein the training data comprises sensor readings collected at one or more sensors of a second bed system.

23 . The system of claim 21 , wherein the system further comprises a pad configured to be used with the bed system.

24 . The system of claim 23 , wherein the pad is configured to be placed on top of a mattress of the bed system.

25 . The system of claim 23 , wherein the pad includes an activatable heating element.

26 . The system of claim 23 , wherein the pad includes an activatable temperature modulation element.

27 . The system of claim 26 , wherein the activatable temperature modulation element is configured to i) actively heat and ii) actively cool.

28 . The system of claim 21 , wherein the one or more sleep stage classifiers were trained at a remote server configured to receive the training data over a data network.

29 . The system of claim 21 , wherein the one or more sleep stage classifiers were trained, using the machine learning techniques and based on the training data, by:

generating feature sets from the training data;

mapping the training data to a kernel space; and

training the one or more sleep stage classifiers with the feature sets so that, based on the training data in the kernel space, the one or more sleep stage classifiers are able to classify unseen data.

30 . The system of claim 21 , wherein the received sensor readings include pressure readings.

31 . A bed system comprising:

a mattress;

one or more sensors; and

a controller configured to be in data communication with the one or more sensors, the controller being configured to:

retrieve one or more sleep stage classifiers, wherein the one or more sleep stage classifiers were trained, using machine learning techniques and based on training data, to generate a respective sleep stage vote when run by the controller on sensor readings;

receive sensor readings from the one or more sensors;

run the one or more sleep stage classifiers on the received sensor readings in order to collect one or more sleep stage votes from the running sleep stage classifiers;

determine, from the one or more sleep stage votes, a sleep stage of a user on the bed system; and

operate the bed system according to the determined sleep stage.

32 . A system comprising:

a pad configured to be placed on top of a mattress of a bed system;

one or more sensors; and

a controller configured to be in data communication with the one or more sensors, the controller being configured to:

retrieve one or more sleep stage classifiers, wherein the one or more sleep stage classifiers were trained, using machine learning techniques and based on training data, to generate a respective sleep stage vote when run by the controller on sensor readings;

receive sensor readings from the one or more sensors;

run the one or more sleep stage classifiers on the received sensor readings in order to collect one or more sleep stage votes from the running sleep stage classifiers;

determine, from the one or more sleep stage votes, a sleep stage of a user on the bed system; and operate the bed system according to the determined sleep stage.

33 . A bed system for sleep stage mitigation, the bed system comprising:

a mattress;

one or more sensors configured to generate one or more data streams indicative of a physiological state of a specific user on the mattress;

at least one physical adjustment mechanism configured to change a physical configuration of the bed system; and

a controller including one or more processors and memory, the controller in data communication with the one or more sensors and the at least one physical adjustment mechanism, the controller configured to:

execute a trained sleep stage determination model, wherein the trained sleep stage determination model is a deep neural network that was trained using historical sensor data collected from a population of users other than the specific user;

process, using the trained sleep stage determination model, the data stream of the specific user to determine a sleep stage state of the specific user;

determine whether the sleep stage state indicates that the specific user is experiencing a particular sleep stage; and

in response to determining the sleep stage state indicates that the specific user is experiencing the particular sleep stage, cause the at least one physical adjustment mechanism to change the physical configuration of the bed system.

34 . The bed system of claim 33 , wherein the at least one physical adjustment mechanism is an articulation mechanism, and wherein changing the physical configuration of the bed system comprises adjusting an incline position of a head portion of the mattress.

35 . The bed system of claim 33 , wherein determining the sleep stage state is further based on a confidence score generated by the trained sleep stage determination model, and wherein the controller causes the at least one physical adjustment mechanism only when the confidence score exceeds a predetermined threshold.

36 . A bed system for sleep stage mitigation, the bed system comprising:

a mattress;

one or more sensors configured to generate one or more data streams indicative of a physiological state of a user on the mattress;

at least one physical adjustment mechanism; and

a controller in data communication with the one or more sensors and the at least one physical adjustment mechanism, the controller configured to:

process the one or more data streams using a plurality of machine learning models to generate a plurality of independent sleep stage votes, wherein each machine learning model in the plurality of machine learning models is configured to generate a respective independent sleep stage vote;

aggregate, using a vote-counting scheme, the independent sleep stage votes from the plurality of machine learning models to determine a final sleep stage state;

determine whether the final sleep stage state indicates that the user is experiencing a particular sleep stage; and

in response to determining that the final sleep stage state indicates that the user is experiencing the particular sleep stage, cause the at least one physical adjustment mechanism to change a physical configuration of the bed system.

37 . The bed system of claim 36 , wherein the vote-counting scheme comprises for each independent sleep stage vote of the independent sleep stage votes from each machine learning model of the machine learning models:

applying a distinct weight to the independent sleep stage vote from the machine learning model based on a historical accuracy of the machine learning model.

38 . A bed system for reliable sleep stage mitigation, the bed system comprising:

a mattress;

one or more sensors configured to generate one or more data streams from sensing a user on the mattress;

at least one physical adjustment mechanism; and

a controller in data communication with the one or more sensors and the at least one physical adjustment mechanism, the controller configured to:

execute a machine learning model to process the one or more data streams;

generate, over a series of successive time windows, a corresponding series of individual confidence scores, each individual confidence score indicating a likelihood of experiencing a particular sleep stage within its respective time window;

aggregate the corresponding series of individual confidence scores from the successive time windows into an aggregated confidence score;

determine whether the aggregated confidence score is greater than a predetermined reliability threshold; and

in response to determining that the aggregated confidence score is greater than the predetermined reliability threshold, cause the at least one physical adjustment mechanism to change a physical configuration of the bed system.

39 . A remote server system for deploying sleep stage detection models, the remote server system comprising:

a network interface configured to communicate with a fleet of bed systems over a network;

one or more processors; and

a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the remote server system to:

receive, via the network interface from the fleet of bed systems, historical sensor data indicative of physiological states of a plurality of users;

train a deep neural network model using the received historical sensor data from the plurality of users to generate a trained sleep stage detection model capable of determining a sleep stage state from real-time sensor data; and

transmit, via the network interface, the trained sleep stage detection model to at least one bed system in the fleet, thereby enabling the at least one bed system to run the trained sleep stage detection model on a controller of the bed system.

40 . A bed system for adaptive sleep stage mitigation, the bed system comprising:

a mattress;

one or more sensors configured to generate one or more data streams from sensing a specific user on the mattress;

at least one physical adjustment mechanism; and

a controller in data communication with the one or more sensors and the at least one physical adjustment mechanism, the controller configured to:

obtain a sleep stage detection model that uses an initial set of operational parameters, wherein the sleep stage detection model was trained using historical sensor data collected from a population of users other than the specific user;

process the one or more data streams, using the sleep stage detection model and the initial set of operational parameters, to determine an initial sleep stage state during an initial time period; and

for each time period in a sequence of time periods after the initial time period:

collect historical usage data associated with the specific user;

generate, based on the historical usage data, a revised set of operational parameters; and

process the one or more data streams, using the sleep stage detection model and the revised set of operational parameters, to determine a sleep stage state during the time period.