Predicting workers' inattentiveness to struck-by hazards by monitoring biosignals during a construction task: A virtual reality experiment. (August 2021)

• Record Type: Journal Article
• Authors: Kim, Namgyun; Kim, Jinwoo; Ahn, Changbum R.
• Is Part Of: Advanced engineering informatics. Volume 49(2021)
• Publication Date: 2021-08
• Subjects: Biosensing -- Behavior prediction -- Inattentive behaviors -- Virtual reality -- Construction safety; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101359
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: At construction workplaces, workers should be consistently attentive to approaching and nearby safety hazards. However, workers tend to allocate most of their attentional resources to a work task and often exhibit inattentive behaviors to hazards, which may lead to serious injuries and fatalities. Predicting construction workers' inattentiveness is thus critical to preventing accidents in construction workplaces. With the advent of biosensing technologies, the potential of using biosignals to predict human behaviors has been proven in various fields of study. However, to date there has been little discussion about utilizing biosignals to predict construction workers' inattentive behaviors. To this end, this study examines whether construction workers' inattentive behaviors can be predicted by assessing biosignal reactivity. A virtual road construction environment was created and used for an experiment to expose participants to a repeated struck-by hazard without risking actual injury. Participants' biosignals (i.e., electrodermal activity, pupil dilation, and saccadic eye movement) and physical engagement in inattentive behaviors were collected and analyzed. The results of statistical analyses revealed significant differences in biosignal reactivities between participants' attentive behaviors (i.e., paying attention to the hazard) and inattentive behaviors (i.e., ignoring the hazard). The outcomes of the machine learning-based behavior classification also indicate the usefulness of predicting inattentive behaviors by monitoring workers' biosignals during a construction task and provide a foundation for the utilization of biosignals in safety management to prevent accidents resulting from inattentive behaviors.