Distinguishing levels of challenge from physiological signals for the robot-assisted rehabilitation system, RehabRoby

Abstract

Investigation into robot-assisted rehabilitation systems, and robot-assisted systems that are capable of detecting patient's emotions and then modifying the rehabilitation task to better suit the patients' abilities by taking account their emotions have gained momentum in recent years. In this paper, our aim is to distinguish whether the subject is under-challenged or over-challenged using psychophysiological signal data collected from biofeedback sensors while executing the tasks with RehabRoby. Initially, features are extracted from the physiological signals (Blood Volume Pulse (BVP), Skin Conductance (SC), and Skin Temperature (ST)). The extracted features are examined in terms of their contribution to the classification of the overstressed/over-challenged, boredom/under-challenged using variance analysis (ANOVA). The most significant features are selected, and various classification methods are used to classify overstressed/over-challenged, boredom/under-challenged.