Physiological Indicators of Task Demand, Fatigue, and Cognition in Future Digital Manufacturing Environments

Argyle, Elizabeth M.; Marinescu, Adrian; Wilson, Max L.; Lawson, Glyn; Sharples, Sarah

Abstract

As Digital Manufacturing transforms traditionally physical work into more system-monitoring tasks, new methods are required for understanding people's mental workload and prolonged capacity for focused attention.

Many physiological measures have shown promise for detecting changes in cognitive state, and recent advances in sensor technology offer minimally-invasive ways to monitor our cognitive activity. Previous research in functional near-infrared spectroscopy, for example, has observed changes in cerebral hemodynamic response during periods of high demand within tasks. This work investigated the relationships among task demand, fatigue, and attention degradation in a sustained attention task, and their effect on heart rate, breathing rate, nose temperature and hemodynamic response in the prefrontal cortex and middle temporal gyrus.

Analysis revealed a small but significant effect of fatigue on heart rate relative to baseline, breathing rate and hemodynamic response. Task demand had a small but significant effect on breathing rate and nose temperature, both relative to baseline, but no difference between levels of demand was observed in heart rate or hemodynamic response. Our results provide insight into what physiological data can tell us about cognitive state, ability to focus, and the impact of fatigue over time.

Citation

Argyle, E. M., Marinescu, A., Wilson, M. L., Lawson, G., & Sharples, S. (2021). Physiological Indicators of Task Demand, Fatigue, and Cognition in Future Digital Manufacturing Environments. International Journal of Human-Computer Studies, 145, Article 102522. https://doi.org/10.1016/j.ijhcs.2020.102522

• Journal Article Type: Article
• Acceptance Date: Aug 18, 2020
• Online Publication Date: Aug 22, 2020
• Publication Date: 2021-01
• Deposit Date: Aug 25, 2020
• Publicly Available Date: Sep 8, 2020
• Journal: International Journal of Human-Computer Studies
• Print ISSN: 1071-5819
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 145
• Article Number: 102522
• DOI: https://doi.org/10.1016/j.ijhcs.2020.102522
• Public URL: https://nottingham-repository.worktribe.com/output/4851297
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1071581920301245