Emotion detection using physiological signals EEG & ECG

Emotion modeling and identification has attracted substantial interest from disciplines including computer science, cognitive science and psychology. Despite the fact that a lot of qualitative studies have been carried out on emotion, less investigated aspects include the quantifying of physiological signals. This paper presents two physiological signals which are ECG and EEG and shows analysis of its emotional properties. A solution based on the short Fourier transform is proposed for the recognition of dynamically developing emotion patterns on ECG and EEG. Features extraction that are used in this paper are Kernel Density Estimation known as (KDE) and Mel-frequency cepstral coefficients known as MFCC. The classifier that is used in this work is Multi-layer Perceptron known as MLP, classification features are based on the valence and arousal. The experimental setup presented in this work for the elicitation of emotions is based on passive valence /arousal. The results shows that the ECG signal has direct relationship with the arousal factor rather than the valence factor. Also, EEG signal using 19 channels reported high accuracy results for determining emotions

A multimodal system for stress detection

Stress is the physiological or psychological response to internal or external factors, which can happen in short or long terms. Prolonged stress can be harmful since it affects the body, negatively, in several ways, thus contributing to mental and physical health problems. Although stress is not simple to properly identify, there are several studied approaches that solidify the existence of a correlation between stress and perceivable human features. In order to detect stress, there are several approaches that can be taken into consideration. However, this task is more difficult in uncontrolled environments and where non-invasive methods are required. Heart Rate Variability (HRV), facial expressions, eye blinks, pupil diameter and PERCLOS (percentage of eye closure) consist in non-invasive approaches, proved capable to accurately identify the mental stress present in people. For this project, the users’ physiological signals were collected by an external video-based application, in a non-invasive way. Moreover, data from a brief questionnaire was also used to complement the physiological data. After the proposed solution was implemented and tested, it was concluded that the best algorithm for stress detection was the random forest classifier, which managed to obtain a final result of 84.04% accuracy, with 94.89% recall and 87.88% f1 score. This solution uses HRV data, facial expressions, PERCLOS and some personal characteristics of the userO stress é a resposta fisiológica ou psicológica a fatores internos ou externos, o que pode acontecer a curto ou longo prazo. O stress prolongado pode ser prejudicial uma vez que afeta o corpo, negativamente, de várias formas, contribuindo assim para problemas de saúde mental e física. Embora o stress não seja simples de identificar corretamente, existem várias abordagens estudadas que solidificam a existência de uma correlação entre o stress e as características humanas percetíveis. De forma a detetar o stress, existem várias abordagens que podem ser tidas em consideração. No entanto, esta tarefa é mais difícil em ambientes não controlados e onde são necessários métodos não invasivos. A variabilidade da frequência cardíaca (HRV), expressões faciais, piscar de olhos e diâmetro da pupila e PERCLOS (fecho ocular percentual) consistem em abordagens não-invasivas, comprovadamente capazes de identificar o stress nas pessoas. Para este projeto, os dados fisiológicos dos utilizadores são recolhidos a partir de uma aplicação externa baseada em vídeo, de forma não invasiva. Além disso, serão também utilizados dados recolhidos a partir de um breve questionário para complementar os dados fisiológicos Após a implementação e teste da solução proposta, concluiu-se que o melhor algoritmo de deteção de stress foi o random forest classifier, que conseguiu obter um resultado final de 84,04% de precision, com 94,89% de recall e 87,88% de f1 score. Esta solução utiliza dados de HRV, expressões faciais, PERCLOS e certas características pessoais do utilizado

Integration of body sensor networks and vehicular ad-hoc networks for traffic safety

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.Peer ReviewedPostprint (author's final draft