Performance metrics outperform physiological indicators in robotic teleoperation workload assessment

Odoh, Gift; Landowska, Aleksandra; Crowe, Emily M.; Benali, Khairidine; Cobb, Sue; Wilson, Max L.; Maior, Horia A.; Kucukyilmaz, Ayse

Abstract

Robotics holds the potential to streamline the execution of repetitive and dangerous tasks, which are difficult or impossible for a human operator. However, in complex scenarios, such as nuclear waste management or disaster response, full automation often proves unfeasible due to the diverse and intricate nature of tasks, coupled with the unpredictable hazards, and is typically prevented by stringent regulatory frameworks. Consequently, the predominant approach to managing activities in such settings remains human teleoperation. Teleoperation can be demanding, especially in high-stress situations, and involves a complex blend of both cognitive and physical workload. We present an experiment to explore a range of physiological and performance-related metrics for workload assessment during robotic teleoperation. Thirty-five participants performed a teleoperation task, during which we manipulated cognitive and physical workload conditions. We recorded multiple metrics, including brain activity using functional Near-Infrared Spectroscopy, galvanic skin responses, cardiovascular responses, subjective workload ratings, task and robot performance data. Our results suggest that robotic teleoperation performance may be the most robust metric for distinguishing between different levels of workload experienced during teleoperation, with most physiological measures becoming insignificant to distinguish high cognitive workload.

Citation

Odoh, G., Landowska, A., Crowe, E. M., Benali, K., Cobb, S., Wilson, M. L., Maior, H. A., & Kucukyilmaz, A. (2024). Performance metrics outperform physiological indicators in robotic teleoperation workload assessment. Scientific Reports, 14(1), Article 30984. https://doi.org/10.1038/s41598-024-82112-4

• Journal Article Type: Article
• Acceptance Date: Dec 2, 2024
• Online Publication Date: Dec 28, 2024
• Publication Date: Dec 28, 2024
• Deposit Date: Dec 28, 2024
• Publicly Available Date: Dec 28, 2024
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Article Number: 30984
• DOI: https://doi.org/10.1038/s41598-024-82112-4
• Keywords: Computer science; Information technology
• Public URL: https://nottingham-repository.worktribe.com/output/43542230
• Publisher URL: https://www.nature.com/articles/s41598-024-82112-4
• Additional Information: Received: 23 August 2024; Accepted: 2 December 2024; First Online: 28 December 2024; : ; : The authors declare no competing interests.