Wearable EEG-based construction hazard identification in virtual and real environments: A comparative study

Abstract

Hazard identification on construction jobsites is one of the most critical components of construction safety management. To overcome the limitations in the current manual practice, researchers explored the correlation between hazard perception and changes in workers’ electroencephalogram (EEG) signals collected from wearable sensors to identify and differentiate different types of hazards. However, most of such studies are based on experiments in a virtual reality (VR) environment. The unproven applicability of the VR-led results to the real jobsites and the potential discrepancies in results between the physical and virtual environments are the main barriers toward EEG-based ubiquitous hazard identification on the jobsites. To bridge the research gaps, this paper tests the feasibility of identifying construction hazards in physical and VR environments, and compares their results to identify discrepancies. Two experimental environments (physical and VR, respectively), each containing three hazards, were designed with the same configuration. Hazard identification task was performed in both experiments, data (e.g., EEG signals) was collected and preprocessed, and corresponding features were extracted. Hotspot analysis was conducted to infer the existence of hazards by assessing spatial associations. The results showed that both physical and VR settings were effective for hazard detection, while a better performance was observed in the VR setting. In addition, the potential of developing hazard zones as well as particular hazard locations and a gradual increase in subjects’ perceived hazard level were revealed. The results highlight the opportunities of revealing potential construction hazards on the construction jobsite using wearable EEG.