Eye-tracking support for analyzing human factors in human-robot collaboration during repetitive long-duration assembly processes

Abstract The integration of eye-tracking technology in manufacturing is emerging as a powerful tool for optimizing human performance and well-being in the workplace. Advances in various fields enabled the recent development of smaller, wearable, and wireless eye-tracking devices which are suitable for naturalistically studying manufacturing processes, such as human-robot collaboration (HRC). However, the implementation of eye-tracking for evaluating mental workload in HRC is still limited, especially in long-duration sessions. This paper provides an overview on the application of eye-tracking technology in the context of cognitive ergonomics within the manufacturing sector, with special attention to eye-tracking metrics and their interpretation relatively to human state in long-duration sessions (i.e., work shifts). In addition, an example case study will be presented to explore the reliability of the most common eye-tracking metrics, concerning a repetitive assembly process of 8 h in an HRC setting. Among the explored eye-tracking metrics, pupil dilation, number and average duration of fixations, and number saccades provided useful insights on the mental strain in dynamic conditions. In addition, from the multiple information gathered by eye-tracking, different phenomena related to mental workload were able to be distinguished. The use of cognitive resources resulting from learning process was well detected by pupil dilation, number of fixations and saccades. Mental fatigue, on the other hand, was well detected by the average duration of fixations and the pupil diameter. These results highlight the need to consider multiple eye-tracking metrics simultaneously to obtain a holistic view of the operator’s psychophysiological state.