Hybrid Kinematic EEG Signal for Workload Monitoring and Evaluation in the Construction Material Mobilization

Yuting Zhang () and Jiayu Chen ()
Additional contact information
Yuting Zhang: Tsinghua University
Jiayu Chen: Tsinghua University

Chapter Chapter 29 in Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, 2024, pp 411-421 from Springer

Abstract: Abstract The construction industry is a typical labor-intensive sector with a high incidence of production accidents and safety risks. Many studies have found that consistently intense workloads could cause workers to lose sufficient vigilance and attention, reduce their risk detection and perception, and lead to the wrong estimation of potential risks and unsafe behaviors. Therefore, monitoring and evaluating the workload of construction personnel will be conducive to assigning, adjusting and controlling the construction process more effectively and safely. Recently, research in many fields has proposed that the EEG signal of employees at work can be obtained through wearable devices in real-time, enabling the quantitative measurement of their workload level. However, the effectiveness of this signal processing framework is often severely affected by EEG “artifacts”. Moreover, the complex construction tasks pose various requirements on the postures and movements of construction workers, making the traditional workload detection method based on static deployment EEG schemes impossible to implement in construction. To address this challenge, this study proposes a new type of hybrid kinematic-EEG signal and corresponding workload indices. This signal preserves the “artifact” triggered by muscle movement in the original signal as a new data feature, creating a more feasible workload monitoring and evaluation scheme for the construction industry. Previous research on occupational accidents in construction has revealed that a significant number of fatalities and injuries are caused by slips, trips, and falls during the mobilization of materials. Therefore, the validation experiments focus on a specific process of material mobilization in construction, aiming to demonstrate the reliability and effectiveness of this approach, which enables a significant advancement in understanding the workload of the construction workers and could have implications for improving overall safety and productivity in the field.

Keywords: Construction; Safety; Workload; Hybrid kinematic-EEG signal; Wavelet packet decomposition (WPD); Entropy (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-97-1949-5_29

Ordering information: This item can be ordered from
http://www.springer.com/9789819719495

DOI: 10.1007/978-981-97-1949-5_29

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().