Using Facial Action Recognition to Evaluate User Perception in Aggravated HRC Scenarios

Abstract

Human-Robot Collaboration (HRC) in the context of industrial workflows becomes more and more important. However, cooperation with powerful industrial robots might be problematic for human workers, who could suffer from fear or irritation. In this paper, we use automatically facial expression recognition, which was trained and evaluated on the AffectNet database, to predict the valence and arousal of 48 subjects during an HRC scenario. This covers an assembly task under regular and three kinds of aggravated conditions. The subjects are divided into two groups: The feedback group that gets automatically information according to the new situation and the no-feedback group that does not. We found that while arousal levels remained unaffected, the no-feedback group showed lower valence under aggravated conditions. This effect was compensated in the feedback group.