Computer Vision and Tactile Glove: A Multimodal Model in Lifting Task Risk Assessment

Details

• Personal Author:
  Chen, Haozhi ; Liu, Peiran ; Lu, Ming-Lun ; Yu, Denny ; Zhou, Guoyang
• Description:
  Work-related injuries from overexertion, particularly lifting, are a major concern in occupational safety. Traditional assessment tools, such as the Revised NIOSH Lifting Equation (RNLE), require significant training and practice for deployment. This study presents an approach that integrates tactile gloves with computer vision (CV) to enhance the assessment of lifting-related injury risks, addressing the limitations of existing single-modality methods. Thirty-one participants performed 2747 lifting tasks across three lifting risk categories (LI < 1, 1≤ LI ≤ 2, LI > 2). Features including hand pressure measured by tactile gloves during each lift and 3D body poses estimated using CV algorithms from video recordings were combined and used to develop prediction models. The Convolutional Neural Network (CNN) model achieved an overall accuracy of 89 % in predicting the three lifting risk categories. The results highlight the potential for a real-time, non-intrusive risk assessment tool to assist ergonomic practitioners in mitigating musculoskeletal injury risks in workplace environments. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Ergonomics Occupational Health Personal Protective Equipment Safety
• Keywords:
  Artificial Intelligence Author Keywords: Computer Vision Biomechanics Gloves Lifting Risk Prediction Motion Studies Neural Networks NIOSH Lifting Equation Risk Assessment Tactile Gloves Wearable Technologies

  More +
• ISSN:
  0003-6870
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Chicago Region [OSHA] ; Indiana ; Ohio
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DFSE - Division of Field Studies and Engineering
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  12 pdf pages
• Volume:
  127
• NIOSHTIC Number:
  nn:20070794
• Citation:
  Appl Ergon 2025 Sep; 127:104513
• Email:
  dennyyu@purdue.edu
• Federal Fiscal Year:
  2025
• NORA Priority Area:
  Manufacturing
• Performing Organization:
  Purdue University
• Peer Reviewed:
  True
• Start Date:
  20240901
• End Date:
  20260831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:dd808dbbc655c1b98bc107087fb066d5d4d92a20a946e31cbcf633d7cfaa9d832b425f1857561fa3e79a0cc0d16e752cd0fbaf64183cc8538ed1d04e4af54625
• Download URL:
  https://stacks.cdc.gov/view/cdc/208948/cdc_208948_DS1.pdf
• File Type:
  [PDF - 5.16 MB ]