Abstract: A bed receives one or more data streams. The bed can provide, as input, sleep-data for the user to a disorder-risk classifier and receive, as output, a disorder-risk metric. The disorder-risk classifier can include a model defining relationships between sleep-data and disorder risk. The bed can use the disorder-risk metric for the user reflective of risk that the user will or is experiencing symptoms consistent with a disorder. The bed can intermittently update, after generating the disorder-risk metric, the disorder-risk metric using the data streams by changing the model.

Abstract: One or more sensors are configured to sense physiological phenomena of a user of the bed; and generate one or more data streams based on the sensing of the physiological phenomena of the user. A computing system may include at least one processor and computer memory, the computing-system configured to: receive the one or more data streams for a plurality of night's sleep for the same user; generate, using the one or more data streams, digital cognitive behavioral therapy for insomnia (dCBTI) parameters for the user; and cause a computing device to operate a dCBTI treatment for the user according to the dCBTI parameters.

Abstract: One general aspect includes a bed having a mattress. The system also includes one or more sensors configured to: sense physiological phenomenon of a user of the bed and generate one or more data streams based on the sensing of the physiological phenomenon of the user. The system also includes a computing system that may include at least one processor and computer memory, the computing-system configured to: receive the one or more data streams and generate, using the one or more data streams, an insomnia-risk metric for the user reflective of risk that the user will or is experiencing symptoms may include with insomnia.

Abstract: A bed has a mattress. One or more sensors are configured to sense one or more physical phenomena of a sleeper on the bed and generate data signals based on the sensed physical phenomena; and send, to a computing system, the data signals. A computing system comprising one or more processors and computer memory. The computing system is configured to: receive the data signals; generate, from data signals of a sleep-session of the sleeper, a feature vector of features, each feature having a feature value that represents one of the physical phenomena; and classify the sleeper into a classified physical state of long COVID for the sleep session based on the feature vector.

Abstract: A bed has a mattress. One or more sensors are configured to sense one or more physical phenomena of a sleeper on the bed and generate data signals based on the sensed physical phenomena; and send, to a computing system, the data signals. A computing system comprising one or more processors and computer memory. The computing system is configured to: receive the data signals; generate, from data signals of a sleep-session of the sleeper, a feature vector of features, each feature having a feature value that represents one of the physical phenomena; and classify the sleeper into a classified physical state for the sleep session based on the feature vector.