Disclosed is a system and method for integrating emotion data into a social network platform and share the emotion data over the social network platform connected through a communication network. 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. The method includes the step of integrating the emotion data through an integration module. The method includes the step of determining an emotional state of the user through an emotional state determination module. The method includes the step of analyzing and displaying emotional data of the users in real-time through an emotional data displaying module.

In one embodiment, a method comprising: (a) receiving a set of physiological data associated with at least one health condition of an animal subject; (b) receiving a set of environmental data associated with one or more environment conditions to which the subject is or has been exposed; (c) determining a set of operating parameters for at least one environmental device based at least partially on at least a portion of the set of physiological data and at least a portion of the set of environmental data; and (d) transmitting the set of operating parameters to the at least one environmental device to at least partially control at least one controlled environmental condition to which the subject is exposed to thereby at partially control the at least one health condition.

A technique for providing biobehavioral rhythm models that generate a series of characteristic features which are further used for measuring stability in biobehavioral rhythms and to predict different outcomes such as health status through a machine learning component. A computational framework is provided for modeling biobehavioral rhythms from mobile and wearable data streams that rigorously processes sensor streams, detects periodicity in data, models rhythms from that data and uses the cyclic model parameters to predict an outcome. The framework can reliably discover various periods of different length in data, extract cyclic biobehavioral characteristics through exhaustive modeling of rhythms for each sensor feature; and provide the ability to use different combination of sensors and data features to predict an outcome.

A wrist temperature rhythm acquisition apparatus and method, a core temperature rhythm acquisition apparatus and method, and a wearable device are provided. The wrist temperature rhythm acquisition apparatus includes a data acquirer configured to acquire wrist temperature data of a user and external environment temperature data; and a processor configured to estimate wrist temperature rhythm data in which an influence of an external environment temperature is corrected, based on the acquired wrist temperature data and the acquired external environment temperature data.

Trigeminal nerves are stimulated based upon pulse counting and chronobiology. A cutaneous electrode assembly is applied to the forehead to stimulate the ophthalmic nerves. A method may include determining a number of pulses to be administered to a patient based upon the disorder being treated, and pulsing current through an electrode assembly to stimulate the patient's supraorbital and supratrochlear nerves with the determined number of pulses. Another method may include determining a pulse repetition frequency for pulses to be administered to a patient based upon the disorder being treated, and pulsing current through an electrode assembly to stimulate the patient's supraorbital and supratrochlear nerves at the pulse repetition frequency.

Methods, systems, and devices for illness detection are described. A method may include identifying baseline temperature data associated with a user based on temperature data collected from the user via a wearable device throughout a first time interval. The method may include receiving additional temperature data collected via the wearable device throughout a second time interval, and inputting the baseline temperature data and the additional temperature data into a classifier. The method may include identifying a satisfaction of deviation criteria between the baseline temperature data and the additional temperature data, and causing a graphical user interface (GUI) of a user device to display an illness risk metric for the user based on the satisfaction of the deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.

Delivering content based on predicted effect can include analyzing, using computer hardware, digital content before providing the digital content to a device of a user, predicting, using the computer hardware, an effect that the digital content will have on a circadian rhythm of the user, based on the predicted effect, modifying, using the computer hardware, the digital content to generate modified digital content, and providing, using the computer hardware, the modified digital content to the device of the user.

In an aspect, a system for a cloud-based user physiology detection software and patient monitoring system. A system includes a wearable device. A wearable device includes a sensor. A sensor is configured to receive physiological data from a user. A system includes a patient monitor configured to monitor a vital sign of a user. A system includes a therapeutic delivery device configured to administer a therapeutic remedy to a user. A system includes a computing device configured to modify a therapeutic remedy of a therapeutic delivery device as a function of physiological data. A method of providing a therapeutic remedy using a cloud-based detection software is also disclosed.

A device for biorhythm detection, includes: a memory; and a processor coupled to the memory and configured to perform operations of: acquiring activity amount data indicating an amount of activity of a test subject which is measured by an activity amount meter configured to measure the amount of activity of the test subject; and detecting, as a frequency derived from a biorhythm of the test subject, a frequency that indicates peak power in a result which is obtained through frequency analysis of a first time waveform of the activity amount data.

Systems and methods for monitoring sleep are disclosed. According to an aspect, a method includes emitting light into tissue. The method also includes detecting light backscattered from the tissue. Further, the method includes determining a sleep pattern based on the backscattered light.