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.

Provided is an electronic device to monitor a user's biological measurements, where a sensor is configured to acquire a first signal from a user, and a diagnostic processor is configured to pre-process the first signal to generate a second signal, segment the second signal to form signal segments, determine at least one event location for each of the signal segments, match adjacent signal segments for feature alignment, and provide a third signal using results of the feature alignment.

A system for delivering sensory stimulation comprises a sensor configured to measure brain activity information of a patient during a sleep session; a sensory stimulator configured to deliver sensory simulation to the patient during the sleep session; and a computer system. One or more physical processors are programmed with computer program instructions which, when executed cause the computer system to: determine a first stimulation profile, a second stimulation profile, or a combination stimulation profile thereof based on obtained sleep cycle information and/or obtained cognitive domain information; and provide input to the sensory stimulator based on the determined stimulation profile, the provided input causing the sensory stimulator to deliver the sensory simulations to the patient based on the determined stimulation profile during the detected slow wave sleep in the patient.

To better fulfill a sleep-inducing function for a seated person, a seat device includes a pressing device provided in a seat back and configured to press a back or a waist of a seated person to induce breathing; at least one of a temperature adjusting device configured to change a temperature of a seat cushion and/or the seat back and a shape adjusting device configured to change a surface shape of the seat cushion and/or the seat back; and a controller configured to control the pressing device to press the back or the waist of the seated person at a set cycle corresponding to a breathing cycle of a person at a sleeping time, and control the at least one of the temperature adjusting device and the shape adjusting device.

According to an aspect, there is provided a computer-implemented method for determining a heart rate of a subject, the method comprising: receiving data representing a signal generated by a pressure sensor configured to be placed on a suprasternal notch of a subject, the data representing a first component of the signal comprising respiratory information associated with the subject and/or a second component of the signal comprising cardiac information associated with the subject; determining, by applying a first algorithm to the data, a respiration parameter of the subject; applying at least one filter to the data to obtain first filtered data, the at least one filter comprising a first filter to attenuate the first component of the signal in the data based on the determined respiration parameter; and determining a heart rate of the subject by applying a second algorithm to the first filtered data.

Methods, systems and devices are provided for motion-activated display of messages on an activity monitoring device. In one embodiment, method for presenting a message on an activity monitoring device is provided, including the following method operations: downloading a plurality of messages to the device; detecting a stationary state of the device; detecting a movement of the device from the stationary state; in response to detecting the movement from the stationary state, selecting one of a plurality of messages, and displaying the selected message on the device.

A system and method are provided for monitoring subject behavior during sleep onset. In some aspects, a system includes one or more sensors configured to acquire behavioral data from a subject using input provided during sleep onset. The system also includes a processor programmed to at least assemble a time-series of behavioral responses using the behavioral data acquired using the one or more sensors, and estimate an instantaneous probability of response using the time-series of behavioral responses. The processor is also programmed to generate a statistical model of wakefulness using the instantaneous probability of response, and estimate, using the model, a probability indicative of a degree to which the subject is awake at each point in time during the sleep onset process. The processor is further configured to generate a report indicative of sleep onset in the subject. The system also includes an output for displaying the report.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

A system for alerting service animals to perform specified tasks generally includes an alert device which provides a first and a second haptic alert from an alert device worn by a service animal and a monitoring device worn by a handler. The monitoring device is programmed to monitor a physiologic parameter of the handler and has a first mode programmed to transmit a first actuation signal to the alert device when the physiologic parameter exceeds a first threshold such that the alert device provides the first haptic alert to the service animal and a second mode programmed to transmit a second actuation signal to the alert device when the physiologic parameter exceeds a second threshold such that the alert device provides the haptic alert to the service animal. The auditory alert is correlated to a first task and the second haptic alert is correlated to a second task.