MoStress: a Sequence Model for Stress Classification

Arturo de Souza, Mateus Begnini Melchiades, Sandro José Rigo, Gabriel de Oliveira Ramos

Abstract: Mental disorders affect a large number of people worldwide. In response to the increasing number of people affected by such illnesses, there has been an increased interest in the use of state-of-the-art technologies to mitigate its effects. This paper presents a Sequence Model for Stress Classification (MoStress), which is a novel pipeline for pre-processing physio-logical data collected from wearable devices and for identifying stress sequences using a recurrent neural network (RNN). Using the WESAD dataset, the RNN model achieved accuracy of 86% in a three-class classification problem (baseline vs. stress vs. amusement). When only considering the presence of stress or not, we achieved an accuracy of 96.5% as well as precision, recall, and f'1-score of 96%, 93%, and 94%, respectively. Those results are close to other papers using the same dataset, however, the neural network used on MoStress, is considerable simpler.