Anxiety detection during human-robot interaction

This paper describes an experiment to determine the feasibility of using physiological signals to determine the human response to robot motions during direct human-robot interaction. A robot manipulator is used to generate common interaction motions, and human subjects are asked to report their response to the motions. The human physiological response is also measured. Motion paths are generated using a classic potential field planner and a safe motion planner, which minimizes the potential collision force along the path. A fuzzy inference engine is developed to estimate the human response based on the physiological measures. Results show that emotional arousal can be detected using physiological signals and the inference engine. Comparison of initial results between the two planners shows that subjects report less anxiety and surprise with the safe planner for high planner speeds.