A method for determining a user wellbeing profile, the method including using a processor to detect a change in physiological data of the user, based on input from a sensor unit and selecting, in accordance with the detected change in physiological data, a specific question relating to psychological data of the user, where user's wellbeing profile can be calculated based on a response from the user to the specific question which was selected in accordance with a specific change in physiological data.

Provided is a data processing apparatus including a signal data processor configured to collect signal data detected through polysomnography, to extract feature data by analyzing a feature of the collected signal data, and to transform the extracted feature data to time series data; and a sleep stage classification model processor configured to input the processed signal data to a pre-generated sleep stage classification model, and to classify a sleep stage corresponding to the signal data. The signal data processor is configured to extract feature data by analyzing a feature of each of an electroencephalographic (EEG) signal, an electro-oculographic (EOG) signal, and an electromyographic (EMG) signal with respect to the signal data, and to transform the extracted feature data to an epoch unit of time series data to input the extracted feature data to the pre-generated sleep stage classification model.

The present disclosure describes a method for neuromodulating a subject, comprising providing (i) a cognitively engaging content, and (ii) a gamma oscillation inducing waveform, wherein said gamma oscillation inducing waveform: comprises an intensity that renders said gamma oscillation inducing waveform imperceptible to said subject; and causes a therapeutic improvement in a cognitive function, thereby neuromodulating said subject.

Methods, systems, and devices for heart rate detection are described. A method may include receiving physiological data associated with the user, where the physiological data may include motion data and temperature data collected throughout a time interval via a wearable device associated with the user. The method may include determining a condition quality metric associated with the time interval based on the received motion data and temperature data. The condition quality metric may indicate a relative quality of the physiological data collected throughout the time interval for determination of heart rate measurements. The method may include sampling photoplethysmogram (PPG) data for the user via the wearable device based on the condition quality metric satisfying a threshold metric value and a timer satisfying a first threshold time duration. The method may include determining a heart rate measurement for the user based at least in part on the sampled PPG data.

A water resistant biometric data logging system for an animal including a housing containing an electrophysiological data logging device and an underwater connector configured to route and waterproof at least 10 electrode cables to the electrophysiological data logging device. Further disclosed is an apparatus (e.g., headcap) for mounting electrodes onto the animal. The apparatus includes a first layer comprising a first plurality of openings or holes, the first layer comprising a foam or a sacrificial material; a second layer comprising a second plurality of openings or holes, the second layer comprising or consisting essentially of synthetic rubber; and a potted piece containing a plurality of electrode cables, wherein the electrode cables are routed from the potted piece through the first plurality of openings and the second plurality of openings or holes.

A method for helping a user awaken from sleep includes receiving information indicating a wake-up time frame; receiving a plurality of biometric readings of the user, each biometric reading including at least one of a heart rate or respiration rate; based on the plurality of biometric readings, determining that the user has had sufficient sleep; and arranging a wake-up sound after entering the wake-up time frame and after determining that the user has had sufficient sleep or at the end of the wake-up timeframe.

A peritoneal dialysis system includes a control unit configured to (i) store a sleep state pattern for the patient, (ii) begin a patient drain followed by a patient fill when at least one sensor indicates that the patient is in a deep sleep state, (iii) extend a dwell period if the sleep state pattern indicates that the patient will enter a subsequent deep sleep state within a first time duration after a programmed dwell period, and (iv) shorten the dwell period if the sleep state pattern indicates that the patient will leave the deep sleep state within a second time duration after an end of the programmed dwell period. The system alternatively or additionally assesses or records a stress level of and/or a fluid/caloric intake by the patient and takes actions accordingly.

A non-transitory computer-readable recording medium records a display program for causing a computer to perform processes of: acquiring data about a space in which a subject sleeps, the data having been detected by a first sensor installed in the space; and, to evaluate and chronologically display suitability of the space as a sleep environment on a basis of the acquired data, switching suitability evaluation criteria before or after a time relating to a sunrise or a sunset when the time is included in a sleep zone of the subject.

A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.

An evaluating apparatus of this application includes a detector configured to detect a biological signal of a user, and a controller configured to calculate an amount of activity of the user from the biological signal, determine whether the user is sleeping during a first period by comparing the amount of activity with a low threshold, and determine whether the user is sleeping during a second period after the first period by comparing the amount of activity with a high threshold, the low threshold being lower than the high threshold.