Methods, systems, and devices for illness detection are described. A method may include identifying physical activity data, sleep data, or both, associated with a user based on physiological data for the user collected via a wearable device throughout a first and second time interval. The method may include inputting the physical activity data, the sleep data, or both, into a classifier, and identifying, using the classifier, a satisfaction of deviation criteria between a subsets of the physical activity data, the sleep data, or both, collected throughout the first and second intervals. The method may further include causing a graphical user interface (GUI) of a user device to display an illness rick metric associated with the user based at least in part on the satisfaction of the deviation criteria, the illness risk metric associated with a probability that the user will transition from a healthy state to an unhealthy state.

Methods, systems, and devices for illness detection are described. A method may include receiving physiological data associated with users, the physiological data being continuously collected via wearable devices associated with the respective users. The method may include identifying baseline physiological data for each user based on a first subset of the physiological data for each respective user. The method may include inputting a second subset of the physiological data and the baseline physiological data for each user into a classifier, and identifying an illness risk metric associated with each user based on the second subset of the physiological data and the baseline physiological data for each respective user. The method may include causing a graphical user interface (GUI) of an administrator user device to display at least one illness risk metric associated with at least one user, the illness risk metric associated with a relative probability that the at least one user will transition from a healthy state to an unhealthy state.

A bio-information processing apparatus includes: an in vivo material measurer which measures an in vivo material of a user; and a processor which extracts a pattern of change of the in vivo material of the user based on a measurement result of the in vivo material, compares the extracted pattern of change of the in vivo material with a biological rhythm reference model, and determines whether biological rhythms of the user are disrupted based on a result of the comparison.

A method, computer program product, and computing system for monitoring one or more environmental variables concerning a client electronic device configured to administer an alertness test to a user. A disrupter is selected for inclusion within the alertness test based, at least in part, upon the one or more environmental variables concerning the client electronic device. The alertness test is administered to the user.

A method and system for guiding a user to improve general performance. The system includes a wearable device to measure at least two parameters associated with the user during an activity period, and a computing device operatively coupled to the wearable device, wherein the computing device is operable to: determine a circadian rhythm of the user based on the measured parameters, classify the user into a chronotype class based on the determined circadian rhythm, determine a typical activity schedule for the user based on the measured parameters, determine an optimal time period in a typical day of the user for sleep and at least one of physical action and cognitive action, receive information about an intended activity of the user, analyse whether the intended activity is within the optimal time period, and guide the user on carrying out the intended activity.

Systems and methods of the present disclosure enable improved seizure detection and/or prediction using a seizure monitoring system. The system receives a data stream including wearable sensor data associated with a user, where the data stream includes electrodermal activity data and where the electrodermal activity data includes circadian rhythm-dependent amplitudes. The system receives a time associated with a seizure of the user. The system trains seizure machine learning model to identify a pre-ictal period associated with a time segment based on the circadian rhythm dependent amplitudes and the time associated with the seizure. The system deploys the seizure machine learning model to ingest a new data stream. Based on the new data stream, the seizure machine learning model predicts a seizure likelihood in a prediction period.

The present invention relates to circadian rhythm management. In order to improve circadian rhythm management, an apparatus is provided that uses an alternative exercise-based concept and personalized approach for managing or controlling personal circadian rhythm and optimizing sleep onset.

This document describes technology comprising of one or more wearable devices (i.e. attached or applied to limbs, body, head or other body extremities but also applicable to implanted or physiologically attachable systems). These systems have a means of enabling diagnostic or prognostic monitoring applicable to monitoring relevant parameters and corresponding analysis determination and characterisation applicable to the onset or detection of events or health conditions of interest. One application relates to sleep monitoring and associate EEG sensors.

Cycles of awakeness and sleep are flexibly but uniquely mapped to calendar days in order to facilitate user interactions with continuously monitored physiological data, and to facilitate meaningful quantitative assessments of sleep, recovery, and physical strain over user cycles that vary above and below twenty four hours in duration.

A method for heart failure management may include volume overload intervention in response to sensor-based parameters indicating volume overload. The method may include administering non-volume overload intervention in response to the sensor-based parameters not indicating volume overload. Volume overload may be determined based on monitoring sensor-based parameters. Sensor-based parameters may be monitored in response to receiving an alert indicative of a worsening heart failure score or status for a patient.