Automated Classification of the Phases Relevant to Work-Related Musculoskeletal Injury Risks in Residential Roof Shingle Installation Operations Using Machine Learning

Details

• Personal Author:
  Breloff SP ; Dai F ; Dutta A ; Mahmud D ; Sinsel EW ; Warren CM ; Wu, John Z.
• Description:
  Awkward kneeling in sloped shingle installation operations exposes roofers to knee musculoskeletal disorder (MSD) risks. To address the varying levels of risk associated with different phases of shingle installation, this research investigated utilizing machine learning to automatically classify seven distinct phases in a typical shingle installation task. The classification process relied on analyzing knee kinematics data and roof slope information. Nine participants were recruited and performed simulated shingle installation tasks while kneeling on a sloped wooden platform. The knee kinematics data were collected using an optical motion capture system. Three supervised machine learning classification methods (i.e., k-nearest neighbors (KNNs), decision tree (DT), and random forest (RF)) were selected for evaluation. The KNN classifier provided the best performance for overall accuracy. The results substantiated the feasibility of applying machine learning in classifying shingle installation phases from workers' knee joint rotation and roof slope angles, which may help facilitate method and tool development for automated knee MSD risk surveillance and assessment among roofers. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Construction Industry Musculoskeletal Diseases Musculoskeletal System Occupational Health Safety
• Keywords:
  Author Keywords: Machine Learning Automated Assessment Biomechanical Engineering Biomechanics Computer-based Methods Computerized Simulation Construction Industry Construction Safety Knee Disorders Motion Studies MSD Musculoskeletal Disorders Musculoskeletal System Disorders Roofers Roofing Industry

  More +
• ISSN:
  2075-5309
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 3 ; OSHA Region 5 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DFSE - Division of Field Studies and Engineering ; HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  13
• Issue:
  6
• NIOSHTIC Number:
  nn:20068037
• Citation:
  Buildings 2023 Jun; 13(6):1552
• Contact Point Address:
  Fei Dai, Wadsworth Department of Civil and Environmental Engineering, West Virginia University, P.O. Box 6103, Morgantown, WV 26506, USA
• Email:
  fei.dai@mail.wvu.edu
• Federal Fiscal Year:
  2023
• NORA Priority Area:
  Construction
• Peer Reviewed:
  True
• Source Full Name:
  Buildings
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7df23f2e970cf1eed137d455754cc792d2d8877da114c822b475fdc71f440cedfbef7d952c43ba801af1a9f05c418afa28b11b8a0ddf58d803b595355cdf2497
• Download URL:
  https://stacks.cdc.gov/view/cdc/230260/cdc_230260_DS1.pdf
• File Type:
  [PDF - 3.06 MB ]