Umer, Waleed, Yu, Yantao, Afari, Maxwell Fordjour Antwi, Anwer, Shahnawaz and Jamal, Arshad (2023). Towards automated physical fatigue monitoring and prediction among construction workers using physiological signals: An on-site study. Safety Science, 166 ,
Abstract
Physical fatigue has been recognized as a serious health and safety risk among construction workers. As a result, numerous studies have endeavored to monitor/predict it using physiological measures. While the results are promising, their methodologies seem inappropriate. First, many studies utilized inappropriate benchmarking methods for physical fatigue monitoring. Importantly, a few of them utilized physical exertion scales as a surrogate for physical fatigue benchmarking. Second, many of them collected data in highly structured tasks in controlled environments. To assess these potential flaws, this research monitored fourteen construction workers' fatigue onsite by gathering physiological measures and fatigue data simultaneously. The results show that while the physical exertion scale was on average moderately correlated with a valid physical fatigue scale (average correlation coefficient 0.65), correlation coefficients varied widely among workers with the lowest of 0.05 and the highest of 0.89. This variation could be attributed to numerous factors including nature of the task, pacing and breaks during work, and individual factors. This might suggest that the physical exertion scale cannot serve as a good surrogate for physical fatigue. Additionally, the results found that workers’ physiological measures were weakly correlated to fatigue than previous laboratory studies. Overall, this study contributes to the body of knowledge by highlighting the methodological issues in the previous studies related to physical fatigue monitoring using physiological measures and the need to re-evaluate the usefulness of the measures, entailing appropriate methods. More importantly, the current study has challenged the status quo for monitoring/predicting fatigue using physiological
Publication DOI: | https://doi.org/10.1016/J.SSCI.2023.106242 |
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Divisions: | College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering |
Additional Information: | © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Publication ISSN: | 0925-7535 |
Last Modified: | 09 Dec 2024 09:09 |
Date Deposited: | 23 Jan 2024 14:32 |
Full Text Link: |
https://publons ... ublon/64831760/ |
Related URLs: |
https://www.sci ... 1844?via%3Dihub
(Publisher URL) |
PURE Output Type: | Article |
Published Date: | 2023-10 |
Published Online Date: | 2023-06-27 |
Accepted Date: | 2023-06-16 |
Authors: |
Umer, Waleed
Yu, Yantao Afari, Maxwell Fordjour Antwi ( 0000-0002-6812-7839) Anwer, Shahnawaz Jamal, Arshad |