Abstract

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 …

Date

2007

Authors

Emily Mower, David J Feil-Seifer, Maja J Mataric, Shrikanth Narayanan

Conference

RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication

Pages

1125-1130

Publisher

IEEE