An electronic apparatus according to various embodiments of the present disclosure may comprise: a sensor unit for sensing the movement of the electronic apparatus; a communication unit for communicating with an external apparatus; an input unit for receiving a user input; an output unit for providing information to a user; and a control unit for outputting a message inducing a particular action to the user through the output unit on the basis of medical information of the user and the movement of the electronic apparatus, or controlling the communication unit to transmit information related to the electronic apparatus to an external apparatus.

A method and system for clustering users using cognitive stress report for classifying stress levels is provided. Detection and monitoring of cognitive stress experienced by users while performing a task is very crucial. The method includes receiving, user evaluated cognitive stress reports and the physiological signals of the user during the performance of the task. A normalized cognitive report is generated from the user evaluated cognitive stress report by computing mode and range value. The normalized cognitive stress reports of the users are used to cluster the users into a primary cluster and a secondary cluster. Feature sets are extracted from the physiological signals of the said users associated with the primary cluster. Using the said feature sets a classifier model is trained to classify the cognitive stress levels of the users as stressful class or stressless class.

The present invention is for a method and system for pain classification and monitoring optionally in a subject that is an awake, semi-awake or sedated.

The present invention is for a method and system for pain classification and monitoring optionally in a subject that is an awake, semi-awake or sedated.

A system and method for controlling a non-tactile device including a receiving device configured to receive signals corresponding to a user's brain waves or movements, the brain waves or movements corresponding to a series of directional intentions, the intentions defining at least one line pattern, a processor configured to process the at least one line pattern, each of said at least one line patterns associated with an action of the device, and output a control signal to the non-tactile device related to the action.

A system and method for controlling a non-tactile device including a receiving device configured to receive signals corresponding to a user's brain waves or movements, the brain waves or movements corresponding to a series of directional intentions, the intentions defining at least one line pattern, a processor configured to process the at least one line pattern, each of said at least one line patterns associated with an action of the device, and output a control signal to the non-tactile device related to the action.

Disclosed is a system and method for measuring a non-stationary brain signal. Per the method, the system receives brain signals, extracts one or more features from the brain signals, determines, based on the Receive brain signals extracted one or more features, a super feature set describing dynamic behaviour of the brain signals, and forms a cluster-recurrent-neural-network (CRNN) from one or more samples taken from the super feature set, by formExtract one or more features ing at least one cluster of the one or more samples based on the one or more from the brain signals features, to estimate a brain state of interest in each cluster of brain signals; using a Monte Carlo approach to estimate an a posteriori probability density function of the brain state of interest by applying the CRNN to each cluster of the at least one cluster; and determining the brain state of interest from the estimated density function.

An intelligent portable medical instrument has an information processing unit and a data storage unit which are connected to a measurement and human body data collection unit. The measurement and human body data collection unit measures electrical, chemical, and acoustic data and sends the data to the information processing unit. The information processing unit compares the measured human physiological index data with the standard ranges of values and makes a preliminary health diagnosis opinion. The preliminary health diagnosis opinion and the measured data are transmitted to an in vitro unit which preferably uploads the information to a cloud server. The in vivo portion of the intelligent portable medical instrument is provided by a single integrated circuit.

An intelligent portable medical instrument has an information processing unit and a data storage unit which are connected to a measurement and human body data collection unit. The measurement and human body data collection unit measures electrical, chemical, and acoustic data and sends the data to the information processing unit. The information processing unit compares the measured human physiological index data with the standard ranges of values and makes a preliminary health diagnosis opinion. The preliminary health diagnosis opinion and the measured data are transmitted to an in vitro unit which preferably uploads the information to a cloud server. The in vivo portion of the intelligent portable medical instrument is provided by a single integrated circuit.

The present disclosure pertains to a system and method for determining sleep stages during individual sleep cycles based on algorithms and/or parameters that correspond to the individual sleep cycles. The system enables more accurate real-time sleep stage determinations compared to prior art systems. Sleep cycles are detected in real-time based on an electroencephalogram (EEG), and/or by other methods. At the end of a sleep cycle, the system is configured such that the specific algorithms and/or parameters used for the previous sleep cycle to determine sleep stages are replaced by new ones which are specifically adapted for the next sleep cycle.