A Single-Camera Method For Estimating Lift Asymmetry Angles Using Deep Learning Computer Vision Algorithms.

Details

• Alternative Title:
  IEEE Transactions on Human-Machine Systems, 2025, v. 55, no. 2: A Single-Camera Method For Estimating Lift Asymmetry Angles Using Deep Learning Computer Vision Algorithms.
• Journal Article:
  IEEE Transactions on Human-Machine Systems
• Personal Author:
  Lou, Zhengyang ; Zhan, Zitong ; Xu, Huan ; Li, Yin ; Hu, Yu Hen ; Lu, Ming-Lun ; Werren, Dwight M. ; Radwin, Robert G.
• Description:
  A computer vision (CV) method to automatically measure the revised NIOSH lifting equation asymmetry angle (A) from a single camera is described and tested. A laboratory study involving ten participants performing various lifts was used to estimate
  
  in comparison to ground truth joint coordinates obtained using 3-D motion capture (MoCap). To address challenges, such as obstructed views and limitations in camera placement in real-world scenarios, the CV method utilized video-derived coordinates from a selected set of landmarks. A 2-D pose estimator (HR-Net) detected landmark coordinates in each video frame, and a 3-D algorithm (VideoPose3D) estimated the depth of each 2-D landmark by analyzing its trajectories. The mean absolute precision error for the CV method, compared to MoCap measurements using the same subset of landmarks for estimating A, was 6.25° (SD = 10.19°, N = 360). The mean absolute accuracy error of the CV method, compared against conventional MoCap landmark markers was 9.45° (SD = 14.01°, N = 360).
• Content Notes:
  Author manuscript
• Subjects:
  • Lifting
  • Low Back Pain
  • Risk Assessment
  • Computers
• Keywords:
  • Computer vision (CV)
  • low back pain
  • manual materials handling
  • NIOSH lifting equation
  • risk assessment
• Source:
  IEEE Transactions on Human-Machine Systems, 2025, v. 55, no. 2
• DOI:
  https://doi.org/10.1109/thms.2025.3539187
• ISSN:
  2168-2291 ; 2168-2305
• Pubmed ID:
  40160534
• Pubmed Central ID:
  PMC11951292
• Document Type:
  Text
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/ ; R01 OH011024/OH/NIOSH CDC HHSUnited States/ ; T42 OH008455/OH/NIOSH CDC HHSUnited States/
• Corporate as Subject:
  National Institute for Occupational Safety and Health, U.S.
• Genre:
  Journal Article
• Pages in Document:
  309-314 (21 pdf pages)
• Volume:
  55
• Issue:
  2
• Download URL:
  https://stacks.cdc.gov/view/cdc/261548/cdc_261548_DS1.pdf
• File Type:
  [PDF - 688.77 KB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:20504bbd71c0a7879f05f2569cc855d4071cfbf921e7e71a0ab6888bf5b613a0529c2e80269e371051d79469e809b10355991e11857be7f81aced61c4aa56cb2