Fusing Imperfect Experimental Data for Risk Assessment of Musculoskeletal Disorders in Construction Using Canonical Polyadic Decomposition

Details

• Personal Author:
  Breloff SP ; Carey RE ; Dai F ; Dutta A ; Sinsel EW ; Warren CM ; Wu, John Z.
• Description:
  Field or laboratory data collected for work-related musculoskeletal disorder (WMSD) risk assessment in construction often becomes unreliable as a large amount of data go missing due to technology-induced errors, instrument failures or sometimes at random. Missing data can adversely affect the assessment conclusions. This study proposes a method that applies Canonical Polyadic Decomposition (CPD) tensor decomposition to fuse multiple sparse risk-related datasets and fill in missing data by leveraging the correlation among multiple risk indicators within those datasets. Two knee WMSD risk-related datasets - 3D knee rotation (kinematics) and electromyography (EMG) of five knee postural muscles - collected from previous studies were used for the validation and demonstration of the proposed method. The analysis results revealed that for a large portion of missing values (40%), the proposed method can generate a fused dataset that provides reliable risk assessment results highly consistent (70%-87%) with those obtained from the original experimental datasets. This signified the usefulness of the proposed method for use in WMSD risk assessment studies when data collection is affected by a significant amount of missing data, which will facilitate reliable assessment of WMSD risks among construction workers. In the future, findings of this study will be implemented to explore whether, and to what extent, the fused dataset outperforms the datasets with missing values by comparing consistencies of the risk assessment results obtained from these datasets for further investigation of the fusion performance. [Description provided by NIOSH]
• Subjects:
  Construction Industry Musculoskeletal Diseases Occupational Health Safety
• Keywords:
  Algorithms Analytical Methods Author Keywords: Tensor Decomposition Construction Construction Safety Construction Workers Data Fusion Knee Injuries MSD Musculoskeletal Disorders Risk Assessment Statistical Quality Control

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• ISSN:
  0926-5805
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  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
• Volume:
  119
• NIOSHTIC Number:
  nn:20060123
• Citation:
  Autom Constr 2020 Nov; 119:103322
• Contact Point Address:
  Amrita Dutta, Department of Civil and Environmental Engineering, West Virginia University, P.O. Box 6103, Morgantown, WV 26506, United States of America
• Email:
  amdutta@mix.wvu.edu
• Federal Fiscal Year:
  2021
• NORA Priority Area:
  Construction
• Peer Reviewed:
  True
• Source Full Name:
  Automation in Construction
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:29ec8e4407e18d146b9bedc89b434479b32e898059f58ae7467f9b8da9c60e5bb135f4e15d76c1651b0d150a8ca01b7d91dc3f10cd4f0c8e423d9909e4fa7be7
• Download URL:
  https://stacks.cdc.gov/view/cdc/224133/cdc_224133_DS1.pdf
• File Type:
  [PDF - 1.02 MB ]