Apparatuses and methods for non-invasively detecting and classifying transcranial electrical signals are disclosed herein. In an embodiment, system for detecting and interpreting transcranial electrical signals includes: a headset including a plurality of electrodes arranged for detection of the user's transcranial electrical signals; a display configured to display information to the user while the user wears the headset; and a control unit programmed to: (i) receive data relating to the transcranial electrical signals detected by the electrodes of the headset; (ii) create a data matrix with the received data; (iii) convert the data matrix into one or more user values; (iv) define a user output state based on the one or more user values; and (iv) cause alteration of an aspect of the display based on the user output state.

The present disclosure relates to technology for estimating bio-information by using a foldable electronic device. The foldable electronic device includes a main body part, having a first main body and a second main body, configured to fold along a folding line; a first sensor provided on the first main body, and configured to obtain a contact image of an object of a user; a second sensor provided on the first main body, and configured to measure a degree of folding of the main body part; and a processor configured to estimate bio-information of the user, based on the contact image of the object and the degree of folding.

A system may use unconscious behaviors and conscious behaviors for recommendations and authentication. A method, system, computer readable storage medium, or apparatus provides for sending stimulus, wherein the stimulus is in the presence of an object, wherein the stimulus comprises video, audio, or text; observing activity of the object, wherein the object comprises human or animal; measuring reaction of the object to the stimulus; classifying the reaction of the object to the stimulus; and transmitting a message based on the classification.

Embodiments of the present disclosure provide systems and methods for training a machine-learning model for predicting emotions from received media data. Methods according to the present disclosure include displaying a user interface. The user interface includes a predefined media content, a plurality of predefined emotion tags, and a user interface control for controlling a recording of the user imitating the predefined media content. Methods can further include receiving, from a user, a selection of one or more emotion tags from the plurality of predefined emotion tags, receiving the recording of the user imitating the predefined media content, storing the recording in association with the selected one or more emotion tags, and training, based on the recording, the machine-learning model configured to receive input media data and predict an emotion based on the input media data.

This disclosure is related to measuring an individual's executive function under both external and internal pressures. A variety of data points and sensor data may be used to measure executive function data, including, but not limited to: first physiology data, user provided engagement factor data, and mental status data These data points may be converted into first physiology data and engagement factor data, which may be further converted into lifestyle factor data to generated, a first user score, a second user (and/or a third user) score—each score measuring performance under various different cognitive tasks or loads. The scores may be used to measure the individual's executive function under various pressure or load situations.

Systems and methods for predicting and treating relapses for neurological conditions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting and treating a clinical neurological condition relapse. The method includes steps for selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition, monitoring, using a cardiac monitor, the heart rate variability of the patient over time, providing an indicator that a relapse is imminent when the heart rate variability of the patient falls below the threshold heart rate variability value, and treating the patient using a transcranial magnetic stimulation device by applying an accelerated theta burst stimulation protocol where the transcranial magnetic stimulation target is the left prefrontal dorsolateral cortex.

An electronic device for providing biometric information is provided. The electronic device includes a sensor module, a memory, and a processor electrically connected to the sensor module and the memory. The processor obtains a biometric signal from the sensor module at a predetermined time interval, determines whether a user is in a first state on the basis of the obtained biometric signal, in case the user is in a first state, obtains a representative value for a respective of the at least one biometric signal, defines the obtained representative value for the respective of the at least one biometric signal as a candidate reference value for a corresponding biometric signal, determines a candidate reference value satisfying a predetermined condition as a first reference value for the corresponding biometric signal, and updates a second reference value previously configured for the corresponding biometric signal on the basis of the first reference value.

Wearable detection system for detecting vulnerability or risk for infection and/or inflammation and by using this prediction of infection and/or inflammation to realise an early and accurate detection of infection and/or inflammation of a homeothermic living organism. The system measures and monitors heart rate and physical activity and generates an alert of vulnerability or an alert of infection and/or inflammation. The detection is based on decomposition of the heart rate in physical, mental and circadian basal heart rate components, calculation of resilience based on evaluating energy expenditure versus recovery and evaluation of change of the circadian basal heart rate component.

An information processing device (14) extracts a feature value which is an acoustic parameter from audio data. The information processing device (14) generates a spectrogram image of the audio data. The information processing device (14) calculates, on the basis of the feature value and a calculation model, a first score which indicates the extent of a user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) inputs the spectrogram image to a learned model, and calculates a second score which indicates the extent of the user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) combines the first score and the second score to calculate a combined score which indicates the extent of the user's psychiatrically-based disorder or neurologically-based disorder, or mental disorder symptom or cognitive dysfunction symptom. The information processing device (14) estimates whether the user has any of the disorders or symptoms according to the combined score.

According to an aspect, there is provided a method of sending a message to a subject, the method comprising obtaining (30; 502) measurements of one or more physiological characteristics of a subject over time; determining (32; 504) a psychological state of the subject for a plurality of time periods from the measurements; and in the event that it is determined that the subject is in a psychological state of interest in a first time period, sending (34; 514) a message to the subject in a time period subsequent to the first time period in which it is determined that the subject is not in the psychological state of interest.