Assessment of construction workers' perceived risk using physiological data from wearable sensors: A machine learning approach. (October 2021)

• Record Type: Journal Article
• Authors: Lee, By Gaang; Choi, Byungjoo; Jebelli, Houtan; Lee, SangHyun
• Is Part Of: Journal of building engineering. Volume 42(2021)
• Publication Date: 2021-10
• Subjects: Construction safety -- Perceived risk -- Wearable sensor -- Physiological signals -- Machine learning; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2021.102824
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Considering that workers' safe or unsafe behaviors are responses to their perceived risk when working, understanding workers' perceived risk is vital for safety management in the construction industry. Existing tools for measuring workers' perceived levels of risk mainly rely on post-hoc survey-based assessments, which are limited by their lack of continuous monitoring ability, lack of objectivity, and high cost. To address these limitations, this study develops an automatic method to recognize construction workers' perceived levels of risk by using physiological signals acquired from wristband-type wearable biosensors in conjunction with a supervised-learning algorithm. The performance of the model was examined with physiological signals acquired from eight construction workers performing their daily work. The model achieved a validation accuracy of 81.2% for distinguishing between low and high levels of perceived risk. This study provides a new means of continuous, objective, and non-invasive method for monitoring construction workers' perceived levels of risk. Highlights: A model to recognize workers' perceived risk using a wearable sensor is developed. The model uses physiological signals acquired from wearable biosensors. The model applies supervised learning algorithms to recognize perceived risk. The performance of the model was examined using field physiological data. Wearable biosensors can feasibly be used to recognize perceived risk during work.