A Data-Driven Fuzzy Logic Method for Psychophysiological Assessment: An Application to Exoskeleton-Assisted Walking

Abstract

Multimodal physiological monitoring and related estimation of the PsychoPhysiological (PP) state play an essential role in investigating the physical and cognitive workload of people executing a motor task. The aim of this work was to develop a data-driven Fuzzy Logic method to estimate four PP indicators, i.e., Energy Expenditure, Fatigue, Attention, and Stress, and test it in a study including ten healthy participants walking while assisted by a lower limb treadmill-based exoskeleton. PP indicators were compared with participants’ self-reported evaluation of the human-robot interaction experience following the administration of a dedicated questionnaire. Results from a correlation analysis demonstrated that the output of the Fuzzy Logic method was consistent with the participants’ subjective assessment.