Temperature sensors are configured to sense a temperature for the sleeper and transmit temperature readings. A controller is configured to receive temperature readings; and send to a heating subsystem instructions to initiate a warming process that raises a distal temperature of the sleeper more than a proximal temperature of the sleeper. The heating subsystem that may include a heating element that, when engaged, can raise the distal temperature of the sleeper more than the proximal temperature of the sleeper.

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

Techniques are provided for performing radio frequency (RF) sensing to determine the viewing status of a television user. This can be used to determine user behavior during the playback of content (e.g., whether a user is watching the content), which can be used as a data point for determining the user's level of interest in the content. Using the status of the television user, embodiments can provide additional or alternative functionality, such as powering down and/or powering up the television. Furthermore, RF sensing may be performed by existing television hardware, such a Wi-Fi transceiver, and may therefore provide RF sensing functionality to a television with little or no added cost.

A method includes receiving physiological data associated with a user during a sleep session. The method also includes determining a sleep-wake signal for the user during the sleep session based at least in part on the received physiological data. The method also includes determining one or more sleep-related parameters for the user during the sleep session based at least in part on the sleep-wake signal. The method also includes determining that the user experienced insomnia during the sleep session based at least in part on at least one of the one or more sleep-related parameters. The method also includes identifying a type for the insomnia experienced by the user based at least in part on the one or more sleep-related parameters.

A sleepiness estimating device including an auxiliary information acquirer that acquires auxiliary information including at least one of five-sense information perceived by a person or emotion information indicating an emotion of the person; and a sleepiness estimator that estimates a sleepiness of the person based on the auxiliary information.

An electronic device according to an embodiment includes a speaker capable of outputting a sound wave, a sensor capable of acquiring an optical signal related to blood flow at a measured part of a user, and a controller configured to measure blood flow information of the measured part based on the optical signal. The controller estimates a sleep state of the user based on the blood flow information and controls, based on the sleep state, a sound wave outputted from the speaker.

A system for displaying information indicative of driving conditions, to a driver, using a smart ring are disclosed. An exemplary system includes a smart ring with a ring band having a plurality of surfaces including an inner surface, an outer surface, a first side surface, and a second side surface. The system further includes a processor, configured to obtain data from a communication module within the ring band, or from one or more sensors disposed within the ring band. The obtained data are representative of information indicative of one or more driving conditions to be displayed to the driver. The smart ring also includes an electronic ink (e-ink) display disposed on at least one of the plurality of surfaces, and configured to present information indicative of the one or more driving conditions.

Systems and methods for assessing compliance with position therapy. In an embodiment, position therapy is provided to a user while the user is wearing a position therapy device. The position therapy comprises, by the device, collecting positional data, determining positions of the user over a time period based on the positional data, and, when it is determined that the user is in a target position, providing feedback to the user to influence the user to change to a non-target position. In addition, the device stores a duration of use in its memory. The duration of use indicates a duration that the user has used the wearable position therapy device in each of one or more positions. An assessment of the user's compliance with the position therapy is then provided based, at least in part, on the duration of use.

A dynamic wake-up alarm is provided-for, including a clock, a contactless biometric sensor, a processor, memory, and a speaker. The processor may be configured to receive a wake-up rule based on at least two wake-up criteria including a time from the clock and data from the biometric sensor, and evaluate whether the criteria are met to activate an alarm.

A sleep monitor for monitoring baby sleep uses sleep state classification based on heartbeat feature respiration features. The sleep monitor automatically retrains the classification during use of the sleep monitor. Training examples for use in this training process are generated automatically by detecting time instants whereat the baby in the bed is in a wake state, based on signals from the at least one of a sound feature detector a movement feature detector (112) and an open eye detector (114). The retraining may comprise using time sequence from the end of detection of wake states to assign a class to heartbeat feature and/or respiration feature values during that time sequence for the training process. In an embodiment, the retraining comprises clustering detected heartbeat feature and/or respiration feature values detected outside the detected wake states.