A Data-Driven Fuzzy Logic Method for Psychophysiological Assessment: An Application to Exoskeleton-Assisted Walking
Christian Tamantini, Francesca Cordella, Nevio Luigi Tagliamonte, I. Pecoraro, Iolanda Pisotta, Alessandra Bigioni, Federica Tamburella, Matteo Lorusso, Marco Molinari, Loredana Zollo
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.