Monitoring workers' attention and vigilance in construction activities through a wireless and wearable electroencephalography system

• Published in: Automation in construction Vol. 82; pp. 122 - 137
• Main Authors: Wang, Di, Chen, Jiayu, Zhao, Dong, Dai, Fei, Zheng, Changjian, Wu, Xiaobing
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2017; Elsevier BV
• Subjects: Attention; Brain; Brain signal processing; Construction accidents & safety; Construction industry; Construction safety; Electroencephalography; Electronic devices; Filtration; Frequencies; Hazards; Monitoring; Occupational safety; Risk perception; Safety management; Spatial distribution; Wearable computers; Wearable devices; Wearable technology; Electroencephalography; Construction safety; Brain signal processing; Wearable devices; Attention
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2017.02.001

As a sector associated with high injury and fatality rates, the construction industry requires constant caution with regard to construction laborers during project execution. Different from people in other industries, construction workers are less sensitive to hazards because of their long-term exposure to risks. Therefore, maintaining construction workers' vigilance and monitoring their attention levels are critical to successful safety management practices. However, current attention-assessing approaches are post hoc and subjective and difficult to implement in construction practice. To address these issues, we propose a wireless and wearable electroencephalography (EEG) system to quantitatively and automatically assess construction workers' attention level through processing human brain signals. To validate the proposed system, we conducted an on-site experiment to analyze the EEG signal patterns when construction workers avoid different obstacles in their tasks. The results suggest EEG signal properties such as frequency, power spectrum density, and spatial distribution can effectively reflect and quantify the construction workers' perceived risk level. Especially, lower gamma frequency bands and the frontal left EEG cluster provide the most direct and observable indications of their vigilance states. These conclusions could facilitate the future implementation of wearable EEG devices through data filtering and channel optimization. •Wearable EEG sensors were used to monitor construction workers' perceived risk.•Topographical maps show the signal patterns at various vigilance levels.•On site experiment was conducted to validate the results.•Vigilance states can be assessed through EEG band powers.