A multimodal Physiological-Based Stress Recognition: Deep Learning Models’ Evaluation in Gamers’ Monitoring Application

Abstract

emotional states detection from physiological signals has many potential applications. In Human-Machine or Human-Human interaction systems, stress detection could provide users with improved services and can be a tool for monitoring and preventing potential stress-related pathologies. Traditional machine learning techniques for automatic stress recognition have been used in previous researches but they sometimes present specific limitations. The emergence of deep learning permits the reveal of underlying patterns in body response witch, otherwise would not be easily observed. In this paper we explore the application of Long Short-Term Memory (LSTM) and Deep Neural (DNN) Networks for real time stress monitoring in young gamers. We base our study on their body responses. For this, we use physiological signals such as the electrocardiography (ECG), the electrodermal activity (EDA), and the electromyography (EMG), measured by non-invasive wearable sensors. The result of the study provides an evaluation of both models’ capacity in predicting real time gamers’ emotional state built on the variation of their physiological parameters.