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Book Chapter

Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring

  • Seungsoo Lee
  • Seongwoo Son
  • Pa Pa Win Aung
  • Minsoo Park
  • Seunghee Park

According to the Ministry of Manpower, falling and slipping accidents are one of the most common accidents in addition, falls from heights (FFH), including accidents during scaffolding work, are still a major cause of death in the construction industry. Regular safety checks are currently being carried out on construction sites, but scaffold-related accidents continue to occur. Sensing technology is being attempted in many industrial sites for safety monitoring, but there are still limitations in terms of the cost of sensors and object detection, which are limited to certain risks. Therefore, this paper proposes a deep learning-based pose estimation approach to identify the risk of falling during scaffolding work in the construction industry. Through analysis of the correlation between unstable behavior during scaffold work and the angle of keypoints of workers, the proposed approach demonstrates the ability to detect the risk of falling. The proposed approach can prevent falling accidents not only by detecting construction site workers, but also by detecting specific risky behaviors. In addition, in limited work environments other than scaffolding work, the information on unstable behavior can be provided to safety managers who may not be aware of the risk, thus contributing to preventing falling accidents

  • Keywords:
  • deep learning,
  • pose estimation,
  • keypoint angle calculate,
  • construction site safe monitoring,
  • falls from heights,
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Seungsoo Lee

Sungkyunkwan University, Korea (the Republic of)

Seongwoo Son

Sungkyunkwan University, Korea (the Republic of)

Pa Pa Win Aung

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0003-2868-6457

Minsoo Park

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0002-5096-3310

Seunghee Park

Sungkyunkwan University, Korea (the Republic of) - ORCID: 0000-0001-8970-0668

  1. Duan, P., Goh, Y.M., Zhou, J. (2023). Personalized stability monitoring based on body postures of construction workers working at heights. Safety Science, 162, 106104
  2. Khan, M., Khalid, R., Anjum, S., Khan, N., Park, C. (2021 August). IMU based Smart Safety Hook for Fall Prevention at Construction Sites. 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Korea, Republic of (pp. 1-6)
  3. Khan, N., Saleem, M.R., Lee, D., Park, M., Park, C., (2021). Utilizing safety rule correlation for mobile scaffolds monitoring leveraging deep convolution neural networks. Computers in Industry 129(103448), 0166-3615
  4. Korea Occupational Safety and Health Agency. (2018). Guidelines for the Prevention of Musculoskeletal Diseases in the Construction Industry. (H-196-2018)
  5. MassirisFernández, M., Fernández, J., Bajo, J., Delrieux, C. (2020). Ergonomic risk assessment based on computer vision and machine learning. Computers & Industrial Engineering, 149, 106816
  6. Park, M., Quoc Tran, Dai. Bak, J., & Park, S. (2023). Small and overlapping worker detection at construction sites. Automation in Construction, 151, 104856.
  7. Roberts, D., Torres Calderon, W., Tang, S., Golparvar-Fard, M. (2020). Vision-based construction worker activity analysis informed by body posture. Journal of Computing in Civil Engineering, 34(4), 04020017.
  8. U.S. BUREAU OF LABOR STATISTICS. (2023). Economic Daily of the Bureau of Labor Statistics. (May, 1th)
  9. Valero, E., Sivanathan, A., Bosche, F., Abdel-Wahab, M. (2016). Musculoskeletal disorders in construction: A review and a novel system for activity tracking with body area network. Applied Ergonomics, 54, 120-130
  10. Xia, N., Zou, P.X.W., Liu, X., Wang, X., Zhu, R. (2018). A hybrid BN-HFACS model for predicting safety performance in construction projects. Safety Science, 101, 332–343.
  11. Xu, M., Nie, X., Li, H., Cheng, J.C.P., Mei, Z. (2022). Smart construction sites: a promising approach to improving on-site HSE management performance. Journal of Building Engineering, 49, 104007
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  • Publication Year: 2023
  • Pages: 641-647

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  • Publication Year: 2023

Chapter Information

Chapter Title

Deep Learning Based Pose Estimation of Scaffold Fall Accident Safety Monitoring

Authors

Seungsoo Lee, Seongwoo Son, Pa Pa Win Aung, Minsoo Park, Seunghee Park

DOI

10.36253/979-12-215-0289-3.63

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

41

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