Citation

Shakerian S, Habibnezhad M, Ojha A, Lee G, Liu Y, Jebelli H, Lee SH. Safety Sci. 2021; 142: e105395.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ssci.2021.105395

PMID

unavailable

Abstract

Construction workers are at a high risk of exposure to excessive heat generated by several factors such as intensive physical activities, personal protective clothing, and frequent heat events at construction sites. Previous studies attempted to evaluate the occupational risk of heat stress by concentrating on environmental variables or the self-assessment measures of perceived heat. Despite their potentials, most of these approaches were intrusive, inaccurate, and intermittent. More importantly, they mainly overlooked the disparities in workers' physical and physiological characteristics. To address these limitations, this study proposes a heat-stress risk-assessment process to evaluate workers' bodily responses to heat - heat strain - based on the continuous measurement of their physiological signals. To this end, workers' physiological signals were captured using a wristband-type biosensor. Subsequently, their physiological signals were decontaminated from noises, resampled into an array of informative features, and finally interpreted into distinct states of individuals' heat strain by employing several supervised learning algorithms. To examine the performance of the proposed process, physiological signals were collected from 18 subjects while performing specific construction tasks under three predetermined environmental conditions with a different probability of exposure to heat stress. The analysis results revealed the proposed process could predict the risk of heat strain with more than 92% accuracy, illuminating the potentials of wearable biosensors to continuously assess workers' heat strain. The long-term implications of this study can be capitalized as guidelines to improve systematic evaluation of heat strain and promote workers' occupational safety and well-being through early detection of heat strain at construction sites.

Language: en

Keywords

Construction Workers' safety; Data-driven health monitoring; Electrodermal activity (EDA); Occupational Heat strain risk; Signal processing; Skin temperature (ST); Supervised learning algorithms; Wearable biosensors