Video-based 3D pose estimation for residential roofing.

Details

• Personal Author:
  Breloff SP ; Carey RE ; Hawke AL ; Li K ; Peng X ; Wang R ; Zheng L
• Description:
  Residential roofers are often exposed to awkward postures and motions in a prolonged time, which may not only reduce their body stability and increase fall potential, but also increase the risk of musculoskeletal disorders (MSDs). To assess their risks of fatal and musculoskeletal injuries, it is crucial to capture 3D body poses of workers during roofing tasks. In this paper, we proposed a novel two-stage motion estimation approach based on a convolution neural network to estimate residential roofer's body poses using three-view video data. Our approach includes two stages: (1) use of an offline multi-view model to estimate the 3D pose in a single frame; (2) use of a multi-frame model to apply temporal convolutions to refine the multi-view outputs. The performance of the approach was evaluated by comparing our estimation with the gold-standard marker-based 3D human pose during one of the common residential roofing tasks - shingle installation. The evaluation results show that the proposed multi-frame model can effectively improve the accuracy of the coordinate sequence. Moreover, these results prove that the proposed video-based motion estimation approach can efficiently and accurately locate 3D body joints and pave the way for future onsite motion analysis during roofing activities. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Construction Industry Ergonomics Musculoskeletal Diseases Musculoskeletal System Occupational Health Safety
• Keywords:
  Author Keywords: 3D Human Pose Estimation; Video-based Motion Prediction; Residential Roofing; Deep Learning Biomechanics; Roofers; Construction Workers; Motion Studies; Body Mechanics; Posture; Neural Networks; Artificial Intelligence; Ergonomics; Musculoskeletal Disorders; MSD;
• ISSN:
  2168-1163
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Delaware ; New Jersey ; OSHA Region 2 ; OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  369-377
• Volume:
  11
• Issue:
  3
• NIOSHTIC Number:
  nn:20065348
• Citation:
  Comput Methods Biomech Biomed Eng Imaging Vis 2023 May; 11(3):369-377
• Contact Point Address:
  Liying Zheng, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
• Email:
  lzheng2@cdc.gov
• Federal Fiscal Year:
  2023
• NORA Priority Area:
  Construction
• Peer Reviewed:
  True
• Source Full Name:
  Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:c65319f5a195ca56db99f32c1f7c717b63c583e44da4e574b61ecaac297562582cb5d87507f686c1cf5bf7abf15e5492c702b9fc039fb73283efe55c232cecb7
• Download URL:
  https://stacks.cdc.gov/view/cdc/248720/cdc_248720_DS1.pdf
• File Type:
  [PDF - 8.15 MB ]