The invention relates to a method for detection of a relapse into a depression or mania state of a patient from a remission state wherein motor activity data is recorded using a wearable device worn by the patient and is received as input data by an evaluating unit and/or mood data is acquired by obtaining a questionnaire which has been completed by the patient, the questions of the questionnaire relating to the mania state, to the depression state and the questionnaire including at least one control question for checking the awareness and/or the ability to focus of the patient, the questions being designed such that they can be answered by multiple choice, and wherein the answers of the patient are input as input data into the evaluating unit, the input data is analyzed by the evaluating unit, wherein the condition of the patient is classified as remission, mania or depression by means of machine learning, and wherein a relapse is detected if the patient is classified as mania or depression.

Further aspects of the invention relate to an evaluating system for detection of a relapse into a depression or mania state of a patient in a remission state based on motor activity data and/or mood data.

Certain examples provide systems and methods to enhance slow wave sleep. An example method includes identifying a sleep stage for slow wave sleep in a subject being monitored. The example method also includes generating, following identification of slow wave sleep and using a processor including a phase locked loop, an output signal based on a phase of a reference input signal, the output signal phase locked according to the reference input signal. The example method includes delivering, during slow wave sleep for the subject, a stimulus to the subject based on the phase locked output signal. The delivering includes providing the stimulus in a series of signal pulses for a first period of time; and providing a refractory period without pulses in a second period of time. The method further includes measuring feedback from the stimulus.

Provided are systems, methods, and devices for implementation of custom sleep parameters. Methods include receiving, via a user interface, a plurality of input parameters associated with a sleep profile of a user, the plurality of input parameters representing at least one sleep profile target, generating, using one or more processors of a processing device, a plurality of sleep parameters based, at least in part, on the received plurality of input parameters, the plurality of sleep parameters representing one or more changes to one or more biomarkers of the user, and generating, using one or more processors of the processing device, a plurality of stimulation parameters based, at least in part, on the plurality of sleep parameters, the plurality of stimulation parameters representing stimuli configured to implement the identified changes for each of the identified biomarkers of the user's sleep profile.

The invention relates to a light sensing device for sensing ambient light intensity, comprising at least one ambient light sensor and an occlusion detector for detecting an object occluding the ambient light sensor. The invention is further related to a corresponding method for sensing ambient light intensity.

A method is for dynamically adjusting a circadian rhythm of an observer via a user device that includes control circuitry and an associated memory. The method includes accessing a calendar, identifying future events to precondition for and determining a preconditioning schedule for at least one of the identified future events. Determining the schedule includes identifying a circadian shift needed for the at least one identified future event, determining a magnitude of the circadian shift, determining a timeframe for preconditioning, determining a magnitude of a per-day shift based upon the timeframe, and determining if the per-day shift exceeds a maximum allowed per-day shift. Also, the method includes, upon a determination that the per-day shift exceeds the maximum allowed per-day shift, setting the preconditioning schedule responsive to the determination, establishing communication between the control circuitry and a light source, and operating the light source to emit light based upon the preconditioning schedule.

There are provided an image acquiring unit (122) for acquiring a plurality of images of a skin of an examination subject which is picked up over a plurality of points of time by an image pickup device (104), a body hair specifying unit (123) for specifying body hair for a growth period of the examination subject from the image with regard to each of the images acquired by the image acquiring unit (122), and a circadian rhythm estimating unit 124 for estimating a circadian rhythm of the examination subject based on a temporal change in a length of the body hair for the growth period which is obtained by specifying the body hair for the growth period from each of the images by the body hair specifying unit 123. Consequently, it is possible to estimate the circadian rhythm of the examination subject from these images by simply acquiring the image of the skin of the examination subject without requiring gene measurement, cell culture, frequent blood collection, analysis in special research facilities or the like.

A system, method and apparatus is disclosed, comprising a biomathetical model for optimizing cognitive performance in the face of sleep deprivation that integrates novel and nonobvious biomathematical models for quantifying performance impairment for both chronic sleep restriction and total sleep deprivation; the dose-dependent effects of caffeine on human vigilance; and the pheonotypical response of a particular user to caffeine dosing, chronic sleep restriction and total sleep deprivation in user-friendly software application which itself may be part of a networked system.

An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

Provided are a method and an apparatus for assessing a degree of alignment between circadian rhythm and life activity rhythm. The method involves sensing a biosignal of a user, deriving a circadian rhythm of the user based on a biosignal change pattern for the biosignal, and calculating a degree of alignment between the circadian rhythm and a life activity rhythm of the user.

Disclosed is a system and method to collect, analyze and share biorhythm data among a plurality of users. The method includes the step of collecting biorhythm data of the user through a wearable user device. The method includes the step of receiving the biorhythm data of the users through a computing device communicatively connected with the wearable user device over the communication network and the step of facilitating the users to access the biorhythm data through a synchronization module. The method includes the step of establishing an interaction with the users over the communication network through an artificial intelligence (AI) based agent module. The method includes the step of analyzing and displaying emotional data of the users in real-time through an emotional data displaying module. The method includes the step of modulating biorhythms of the users based on the feedback emitted from the computing device through a feedback module.