A computer-implemented cognitive assessment tool is provided for assessing cognitive ability of an individual while multi-tasking. In one embodiment, a computer processing system on which the tool is implemented may receive form the individual first responses to a first task and second responses to a second task, where the first task and the second task are presented to the individual simultaneously. The system may determine that the first task and the second task are performed by the individual based on the first responses and the second responses, and compute a cognitive measure using one or both of the first responses and the second responses. Further, computing the cognitive measure may be based on performance measures of one or both of the first responses and the second responses. Based on the cognitive measure, the system may output a cognitive assessment to the individual.

Multi-state engagement with continuous glucose monitoring (CGM) systems is described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains packages of glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform generates state information for the user by processing these CGM packages and the additional data, at least in part, by using one or more models. Based on this state information, the data analytics platform controls communication with the user, which may include generating intervention strategies to prevent users from transitioning to a negative state such as discontinuing use of the CGM system.

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.

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.

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.