An emotion analysis method and an electronic apparatus thereof are provided. The emotion analysis method is adapted to the electronic apparatus having a database or connected to the database in order to analyze an emotion of an examinee. The emotion analysis method includes: obtaining a heart rate signal of the examinee; defining a plurality of candidate emotions from the database; analyzing the heart rate signal to obtain a plurality of target emotion parameters; and analyzing the target emotion parameters to determine one of the candidate emotions corresponding to the heart rate signal by applying an emotion analysis model.

A method of identifying chronic pain in a patient including using functional magnetic resonance imaging (fMRI), performing a functional brain scan in the NAc (nucleus accumbens) of a patient brain including extracting activity from the NAc. Database information, which includes fMRI data obtained from healthy patients, may be compared to the extracted activity to determine if patient is a chronic pain patient. In patients with chronic pain, the method may be repeated to evaluate the effects of the treatment. A resting state brain scan may be performed initially, and a Fourier transform may be performed to obtain frequency content. The frequency bands of the method may be broken down to extract information in a 0.01-0.027 Hz frequency band.

A mobile device, methods and systems provide the invention mobile Personal Health Records (PHR) management platform solution. The platform enables secure PHR data management, measuring user's medical parameters, managing PHR secured depository containing user's health data on the user's invention combined phone & add-on sleeve device, while blocking none legitimate users access to the invention devices secured storage content. The invention device user's authentication is based on the combined weighted fusion of at least two different human biological sensors within the device and their weighted output analysis. The multi-sensors ensure bio-authentication secured memory entry only for the legitimate device user. In case of authentication success it activates various types of applications on the user PHR data depository content stored in device. The system supports the user's PHR remote health management, remotely monitoring the user's measured medical parameters, updating & managing user's health medical history depository in the user's electronic sleeve.

Aspects of the disclosure can provide a method and device for detecting EEG signals of a first person in proximity to the device. The device can include a non-contact EEG directional circuit having non-contact sensors, the non-contact EEG directional circuit being configured to detect the EEG signals produced by a brain of the first person without making contact with the first person. The device can further include a processor coupled to the non-contact EEG directional circuit that is configured to analyze the EEG signals to detect patterns in the EEG signals that correspond to a state of the first person in proximity to the non-contacting sensor and feedback device that is configured to provide a second person with an indication of the state of the first person in proximity to the non-contacting sensor. Additionally, the device can include a contact EEG circuit having sensors that are in contact with the second person and that is configured to detect second EEG signals produced by a brain of the second person, wherein the processor is coupled to the contact EEG circuit and is configured to analyze the second EEG signals to detect patterns in the second EEG signals that correspond to a state of second the person.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

There is described a method and system for measuring a level of anxiety. Measured data comprising EEG data collected from a parietal (P) EEG electrode is received. A group 8 indicator based on a power, P-power (dt), associated with a delta-theta frequency band, dt, within a delta-theta frequency range is extracted. Based on said group 8 indicator, a level of anxiety, LoA, is determined which is a value indicative of the level of anxiety of the subject.

A message selection device according to an embodiment includes a message database that holds a plurality of messages in correspondence with each of various emotion of a communicator, a communicator information acquisition unit configured to acquire communicator information for estimating the emotion of the communicator, a receiver information acquisition unit that acquires receiver information for estimating the emotion of a receiver who is to receive a message from the communicator, and a message selection unit that estimates the emotion of the communicator based on the communicator information acquired by the communicator information acquisition unit, estimates the emotion of the receiver based on the receiver information acquired by the receiver information acquisition unit, and selects a message from among the messages held by the message database based on the estimated emotions.

Generating emotionally enhanced transcription of non-textual data and an enriched visualization of transcribed data by capturing non-textual data of a speaker using bio-feedback technology, transcribing it into to a textual format, combining transcribed textual data with emotional state of the speaker to generate the emotionally enhanced transcribed textual data, and presenting emotionally enhanced transcribed textual data through an enriched visualization including color-coding transcribed textual data to identify mistakes in the transcribed data.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.