Selection of wearable sensor measurements for monitoring and managing entry-level construction worker fatigue: a logistic regression approach
Engineering, Construction and Architectural Management
ISSN: 0969-9988
Article publication date: 1 July 2021
Issue publication date: 16 August 2022
Abstract
Purpose
The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors.
Design/methodology/approach
Twenty-two individuals were assigned different task workloads in repeated sessions. Stepwise logistic regression was used to identify the most parsimonious fatigue prediction model. Heart rate variability measurements, standard deviation of NN intervals and power in the low-frequency range (LF) were considered for fatigue prediction. Fast Fourier transform and autoregressive (AR) analysis were employed as frequency domain analysis methods.
Findings
The log-transformed LF obtained using AR analysis is preferred for daily fatigue management, whereas the standard deviation of normal-to-normal NN is useful in weekly fatigue management.
Research limitations/implications
This study was conducted with entry-level construction workers who are involved in manual material handling activities. The findings of this study are applicable to this group.
Originality/value
This is the first study to investigate all major measures obtainable through electrocardiogram and actigraphy among current mainstream wearables for monitoring occupational fatigue in the construction industry. It contributes knowledge on the use of wearable technology for managing occupational fatigue among entry-level construction workers engaged in material handling activities.
Keywords
Acknowledgements
This article is written based on the first author’s master’s thesis in the School of Public Health program at the University of Washington, United States, Lee (2018).
Citation
Lee, W., Lin, K.-Y., Johnson, P.W. and Seto, E.Y.W. (2022), "Selection of wearable sensor measurements for monitoring and managing entry-level construction worker fatigue: a logistic regression approach", Engineering, Construction and Architectural Management, Vol. 29 No. 8, pp. 2905-2923. https://doi.org/10.1108/ECAM-02-2021-0106
Publisher
:Emerald Publishing Limited
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