A Deep Learning Approach for Lower Back-Pain Risk Prediction During Manual Lifting

Details

• Personal Author:
  Barim, Menekse S. ; Hayden, Marie ; Jha, Rashmi ; Lu, Ming-Lun ; Snyder, Kristian ; Thomas, Brennan ; Werren, Dwight
• Description:
  Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Musculoskeletal System Occupational Health Safety
• Keywords:
  Biomechanics Body Mechanics Low Back Pain Manual Lifting Neural Networks Workers Compensation
• ISSN:
  1932-6203
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 5
• 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:
  22 pdf pages
• Volume:
  16
• Issue:
  2
• NIOSHTIC Number:
  nn:20062196
• Citation:
  PLoS One 2021 Feb; 16(2):e0247162
• Email:
  snyderks@mail.uc.edu
• Federal Fiscal Year:
  2021
• Peer Reviewed:
  True
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0301fbd312bcfa27b4407fa6bfaa8809f4bd27852e13ea81ef2cdd07b2e8dd06cd49e86d9d1e99d70a7593eb15be18f41b6d00ad9cb346156b6f9b1c85f14e2d
• Download URL:
  https://stacks.cdc.gov/view/cdc/225492/cdc_225492_DS1.pdf
• File Type:
  [PDF - 3.22 MB ]