Operator Workload and Teleoperation Interface Design in Remote Maintenance Tasks: Sensor Validation
Keywords:
Teleoperation Interfaces, Cognitive Workload, Sensor Validation, Remote Maintenance, Operator WorkloadAbstract
The execution of remote maintenance tasks through teleoperation interfaces presents significant challenges regarding operator cognitive and physical workload. As industrial applications expand into increasingly hazardous environments, the reliance on advanced teleoperation systems has grown, necessitating interfaces that do not overwhelm human perceptual or cognitive capacities. This comprehensive study investigates the assessment of operator workload by leveraging sensor validation evidence to evaluate different teleoperation interface designs. Utilizing a combination of physiological sensors, including electrodermal activity, photoplethysmography, and binocular eye tracking, the research quantifies the workload experienced by operators performing complex simulated remote maintenance tasks. The study compares a traditional visual-only teleoperation interface with an advanced multimodal interface that incorporates haptic feedback and augmented reality overlays. Through rigorous experimental protocols and comprehensive data analysis, the findings demonstrate that the advanced multimodal interface significantly mitigates operator cognitive load and enhances situational awareness, as evidenced by stabilized physiological metrics and improved task performance. The sensor validation framework provides empirical evidence that mapping multisensory feedback to human perceptual channels reduces the cognitive bottleneck traditionally associated with remote manipulation. The results offer critical insights for the future design and deployment of teleoperation systems in high-stakes industries, emphasizing the necessity of integrating real-time physiological monitoring to adaptively manage operator workload and ensure operational safety.References
1. Lubell, M.; Robbins, M. Adapting to sea-level rise: Centralization or decentralization in polycentric governance systems? Policy Stud. J. 2022, 50, 143–175.
2. Samaniego, J.F. From Hyperconnectivity to AI: How Can We Tackle the Environmental Impact of the New Internet Era? Available online: https://www.uoc.edu/en/news/2023/080-sustainable-digitalization (accessed on 24 June 2026).
3. Abdolkhani, N.; Hamouda, W. Hierarchical Deep Reinforcement Learning for Robust Access in Cognitive IoT Networks under Smart Jamming Attacks. In Proceedings of the IEEE Global Communications Conference (GlobeCom), Taipei, Taiwan, 7–11 December 2025.
4. Zuo, P.; Miao, C.; Fu, C.; Wang, X.; Liu, X.; Liu, B. SMAPPO: A Security-Aware Multi-Agent Reinforcement Learning Framework for Secure Computation Offloading in SAGIN. J. King Saud Univ. Comput. Inf. Sci. 2025, 37, 336.
5. Lee, Y.; Lee, I.W. A longitudinal network analysis of intergovernmental collaboration for local economic development. Urban Aff. Rev. 2022, 58, 229–257.
6. Van Atteveldt, W., Trilling, D., & Calderón, C. A. (2022). Computational analysis of communication: A practical introduction to the analysis of texts, networks, and images. Wiley.
7. Ellis, B.; Cook, J.; Moalla, S.; Samvelyan, M.; Sun, M.; Mahajan, A.; Foerster, J.N.; Whiteson, S. SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, LA, USA, 10–16 December 2023; Neural Information Processing Systems Foundation, Inc.: La Jolla, CA, USA, 2023; pp. 37567–37593.
8. Paliszkiewicz, J.; Koohang, A. Organizational trust as a foundation for knowledge sharing and its influence on organizational performance. Online J. Appl. Knowl. Manag. 2013, 1, 116–127.
9. Yin, R.K. Getting started: How to know whether and when to use the case study as a research method. In Case Study Research: Design and Methods, 5th ed.; SAGE Publications: Thousand Oaks, CA, USA, 2014; pp. 3–26.
10. Barley, S.R.; Tolbert, P.S. Institutionalization and structuration: Studying the links between action and institution. Organ. Stud. 1997, 18, 93–117.
11. Bokobza, Y.; Dabora, R.; Cohen, K. Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks. IEEE Trans. Wirel. Commun. 2023, 22, 4930–4946.
12. Li, Y.; Chang, Y.; Fukawa, K.; Kodama, N. Reinforcement Learning-Based Cognitive Radio Transmission Scheduling in Vehicular Systems. In Proceedings of the 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, 20–23 June 2023; pp. 1–5.
13. Orr, J.; Dutta, A. Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey. Sensors 2023, 23, 3625.
14. Hoffpauir, K.; Simmons, J.; Schmidt, N.; Pittala, R.; Briggs, I.; Makani, S.; Jararweh, Y. A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services. J. Data Inf. Qual. 2023, 15, 1–30.
15. Roulston, K. Analysing interviews. In The SAGE Handbook of Qualitative Data Analysis; Flick, U., Ed.; SAGE Publications Ltd.: London, UK, 2014; pp. 297–313.
16. Iqbal, A.; Al-Habashna, A.; Wainer, G.; Boudreau, G. Sum Rate Maximization in RIS-Assisted Multi-User MISO Systems: A Proximal Policy Optimization-Based Approach. Phys. Commun. 2026, 74, 102961.
17. Schreier, M. Qualitative content analysis—How to do qualitative content analysis? In The SAGE Handbook of Qualitative Data Analysis; Flick, U., Ed.; SAGE Publications Ltd.: London, UK, 2014; pp. 170–184.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), unless otherwise stated.
