International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
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  • Pages.902-908
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  • 2024
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  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Challenges and Improvement Methods for Monitoring Workload of Construction Workers through EEG

  • Yuting Zhang (School of Civil Engineering, Tsinghua University) ;
  • Jiayu Chen (School of Civil Engineering, Tsinghua University)
  • Published : 2024.07.29
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Abstract

A large number of construction accidents are caused by workers' unsafe behavior under excessive workload. Despite the demonstrated effectiveness and advantages of current portable electroencephalogram (EEG) devices in workload monitoring, accurate data acquisition remains challenging due to motion artifacts in dynamic environments. Consequently, most current research is limited to static conditions, thus restricting its application to construction tasks that inherently involve bodily movements. In this study, an innovative signal filtering framework is introduced that employs the principles of adaptive filtering to integrate acceleration signals containing motion information for the correction of motion artifacts in EEG signals. The experimental results demonstrate that this approach effectively eliminates motion-induced artifacts in EEG signals, thereby improving the preprocessing of hybrid kinematic-EEG signals acquired during bodily and muscular movements. By enhancing signal quality and reliability, this preprocessing framework aims to broaden the application of portable EEG devices for real-time workload monitoring among construction workers. This advancement is expected to enhance the practicality of EEG in construction safety management and ultimately contribute to safer construction practices.

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References

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