Monitoring Stress Level Through EDA by Using Convex Optimization

This study designed an effective system for monitoring stress levels based on Electrodermal Activity (EDA) sensors by knowing the signal patterns and characteristics using convex optimization decomposition. The EDA sensor considered accurate and sensitive for identifying stress by analyzing the skin conductivity (SC) due to the changes in sympathetic nerve activity. However, the SC signal consists of phasic and tonic components, which needed to decompose to analyze stress levels. The SC signals also followed by the white Gaussian noise, which represents the error value. Hence, deconvolution is a crucial stage for the further process because the quality of the measurement depends on this result. This study aims to deconvolve the SC signal using the convex optimization method (cvxEDA). This model is physiology inspired by EDA based on Bayesian statistics, convex mathematical optimization, and sparsity. This research conducted with 18 subjects through three sessions of measurement. The given stimuli arise in each session to increase the level of stress. The results showed that this method could separate and identify SC. The Phasic component shows an increase in the stimulus of each session, as seen from the number of peaks. In contrast, there were no significant differences in the tonic component. This study shows that the phasic component is closely related to changes in sudomotor nerve activity (SMNA) and response to a stressor, which could be useful to classify stress levels in the future study.