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How Stress and Mental Workload are Connected

Alsuraykh, Norah H.; Wilson, Max L.; Tennent, Paul; Sharples, Sarah

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Authors

Norah H. Alsuraykh

SARAH SHARPLES SARAH.SHARPLES@NOTTINGHAM.AC.UK
Professor of Human Factors



Abstract

Mental Workload (MWL) can be both good and bad; we can thrive under high MWL, or our performance can drop if the demands become either too low or too high. Similarly, stress is not always bad, short term stress can be beneficial to overcome a challenge or dangerous situation. In our research, we have seen both people that enjoy high workload, and people that feel stressed by it, but we do not know whether that experience of stress significantly affects our measurements. Our recent results show that fNIRS measurements are affected by stress (measured by SSSQ). This paper seeks to discuss the relationship between these concepts, discussing examples of where similar influencing factors appear within models of both Stress and Mental Workload, as well as within subjective measures of them. We conclude that future work must consider participants' experiences of both Stress and Mental Workload, as well as other cognitive concepts, when trying to estimate them from physiological measures.

Citation

Alsuraykh, N. H., Wilson, M. L., Tennent, P., & Sharples, S. (2019). How Stress and Mental Workload are Connected. In PervasiveHealth'19: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (371-376). https://doi.org/10.1145/3329189.3329235

Conference Name PervasiveHealth'19: The 13th International Conference on Pervasive Computing Technologies for Healthcare
Conference Location Trento, Italy
Start Date May 20, 2019
End Date May 23, 2019
Acceptance Date Apr 8, 2019
Online Publication Date May 20, 2019
Publication Date May 20, 2019
Deposit Date Jun 5, 2019
Publicly Available Date Mar 28, 2024
Publisher Association for Computing Machinery (ACM)
Pages 371-376
Book Title PervasiveHealth'19: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare
ISBN 9781450361262
DOI https://doi.org/10.1145/3329189.3329235
Public URL https://nottingham-repository.worktribe.com/output/2145957
Publisher URL https://dl.acm.org/citation.cfm?id=3329235

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