Investigating Implicit Cues for User State Estimation in Human-Robot Interaction Using Physiological Measurements

Achieving and maintaining user engagement is a key goal of human-robot interaction. This paper presents a method for determining user engagement state from physiological data (including galvanic skin response and skin temperature). In the reported study, physiological data were measured while participants played a wire puzzle game moderated by either a simulated or embodied robot, both with varying personalities. The resulting physiological data were segmented and classified based on position within trial using the K-Nearest Neighbors algorithm. We found it was possible to estimate the user´s engagement state for trials of variable length with an accuracy of 84.73%. In future experiments, this ability would allow assistive robot moderators to estimate the user´s likelihood of ending an interaction at any given point during the interaction. This knowledge could then be used to adapt the behavior of the robot in an attempt to re-engage the user.