Estimation of Situation Awareness Score and Performance Using Eye and Head Gaze for Human-Robot Collaboration

Human attention processes play a major role in the optimization of human-robot collaboration (HRC) [Huang et al. 2015]. We describe a novel methodology to measure and predict situation awareness from eye and head gaze features in real-time. The awareness about scene objects of interest was described by 3D gaze analysis using data from eye tracking glasses and a precise optical tracking system. A probabilistic framework of uncertainty considers coping with measurement errors in eye and position estimation. Comprehensive experiments on HRC were conducted with typical tasks including handover in a lab based prototypical manufacturing environment. The gaze features highly correlate with scores of standardized questionnaires of situation awareness (SART [Taylor 1990], SAGAT [Endsley 2000]) and predict performance in the HRC task. This will open new opportunities for human factors based optimization in HRC applications.