Stress Detection from Surface Electromyography using Convolutional Neural Networks

The study of stress and its implications has been the focus of interest in various fields of science. Many automated/semi-automated stress detection systems based on physiological markers have been gaining enormous popularity and importance in recent years. Such non-voluntary physiological features exhibit unique characteristics in terms of reliability, accuracy. Combined with machine learning techniques, they offer a great field of study of stress identification and modelling. In this study, we explore the use of Convolutional Neural Networks (CNN) for stress detection through surface electromyography signals (sEMG) of the trapezius muscle. One of the main advantages of this model is the use of the sEMG signal without computed features, contrary to classical machine learning algorithms. The proposed model achieved good results, with 73% f1-score for a multi-class classification and 82% in a bi-class classification.