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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

102 Scopus Citations
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  • Di Wang
  • Dong Zhao
  • Fei Dai
  • Changjian Zheng
  • Xiaobing Wu


Original languageEnglish
Pages (from-to)122-137
Number of pages16
Journal / PublicationAutomation in Construction
Online published18 Mar 2017
Publication statusPublished - Oct 2017


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.

Research Area(s)

  • Attention, Brain signal processing, Construction safety, Electroencephalography, Wearable devices

Citation Format(s)