EDA, PPG and Skin Temperature as Predictive Signals for Mental Failure by a Statistical Analysis on Stress and Mental Workload

Abstract

Objective: The growth of autonomous systems interacting with humans leads to assessing operators' stress and mental workload (MWL), especially in safety-critical situations. Therefore, a system providing information about the psychophysiological workers' condition is fundamental and still missing. This paper aims to study the statistical relationship between the variation of Photoplethysmogram signal (PPG), Electrodermal Activity (EDA), and skin temperature with respect to stress and MWL levels, assessed through an ad-hoc developed subjective questionnaire. Results: 43 features were calculated from these signals during the execution of two cognitive tests and processed through a statistical analysis based on Kruskal-Wallis and Mann-Whitney U tests. This analysis proved that about 50% of them offered statistical evidence in differentiating relaxed and altered emotional conditions. Moreover, fifteen features were found to provide sufficient information to detect at the same time stress and MWL. Conclusions: These results demonstrate the feasibility of this approach and push to continue this research about the relationship between physiological signals and the variation of stress and MWL by enhancing the population and considering more biosignals.