Automatic Biomechanical Workload Estimation for Construction Workers by Computer Vision and Smart Insoles

Yantao Yu, Heng Li, Waleed Umer, Chao Dong, Xincong Yang, Martin Skitmore, Arnold Y.L. Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

Abstract

Construction workers are commonly subject to ergonomic risks due to awkward working postures or lifting/carrying heavy objects. Accordingly, accurate ergonomic assessment is needed to help improve efficiency and reduce risks. However, the diverse and dynamic nature of construction activities makes it difficult to unobtrusively collect worker behavior data for analysis. To address this issue, an automatic workload approach is proposed for the first time to continuously assess worker body joints using image-based three-dimensional (3D) posture capture smart insoles, and biomechanical analysis to provide detailed and accurate assessments based on real data instead of simulation. This approach was tested in an experiment, indicating that the method was able to automatically collect data concerning the workers' 3D posture, estimate external loads, and provide the estimated loads on key body joints with an error rate of 15%. In addition to helping prevent construction workers' ergonomic risks, the method provides a new data collection approach that may benefit various behavior research fields related to construction safety and productivity management.

Original languageEnglish
Article number04019010
JournalJournal of Computing in Civil Engineering
Volume33
Issue number3
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Automated image-based three-dimensional (3D) posture estimation
  • Biomechanical analysis
  • Construction
  • Deep learning
  • Ergonomic risks
  • Machine learning
  • Occupational health and safety
  • Smart insoles
  • Worker
  • Workload

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

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