Human-Robot Trust Assessment Using Motion Tracking & Galvanic Skin Response

Abstract

In this study we set out to design a computer vision-based system to assess human-robot trust in real time during close-proximity human-robot collaboration. This paper presents the setup and hardware for an augmented reality-enabled human-robot collaboration cell as well as a method of measuring operator proximity using an infrared camera. We tested this setup as a tool for assessing trust through physical apprehension signals in a collaborative drawing task, where participants hold a piece of paper on a table while the robot draws between their hands. Midway through the test we attempt to induce a decrease in trust with an unexpected change in robot speed and evaluate subject motions along with self-reported trust and emotional arousal through galvanic skin response. After performing the experiment with forty participants, we found that reported trust was significantly affected when robot movement speed was increased. The galvanic skin response measurement were not significantly different between the test conditions. The motion tracking method used in this study did not suggest that subjects' motions were significantly affected by the decrease in trust.