Using physiological signals to detect natural interactive behavior

Many researchers in the Human Robot Interaction (HRI) and Embodied Conversational Agents (ECA) domains try to build robots and agents that exhibit human-like behavior in real-world close encounter situations. One major requirement for comparing such robots and agents is to have an objective quantitative metric for measuring naturalness in various kinds of interactions. Some researchers have already suggested techniques for measuring stress level, awareness etc using physiological signals like Galvanic Skin Response (GSR) and Blood Volume Pulse (BVP). One problem of available techniques is that they are only tested with extreme situations and cannot according to the analysis provided in this paper distinguish the response of human subjects in natural interaction situations. One other problem of the available techniques is that most of them require calibration and some times ad-hoc adjustment for every subject. This paper explores the usefulness of various kinds of physiological signals and statistics in distinguishing natural and unnatural partner behavior in a close encounter situation. The paper also explores the usefulness of these statistics in various time slots of the interaction. Based on this analysis a regressor was designed to measure naturalness in close encounter situations and was evaluated using human-human and human-robot interactions and shown to achieve statistically significant distinction between natural and unnatural situations.