Machine Learning for Detection and Risk Assessment of Lifting Action

Details

• Personal Author:
  Bertrand J ; Jha R ; Lu M-L ; Thomas B
• Description:
  Repetitive occupational lifting has been shown to create an increased risk for incidence of back pain. Ergonomic workstations that promote proper lifting technique can reduce risk, but it is difficult to assess the workstations without constant risk monitoring. Machine learning systems using inertial measurement unit (IMU) data have been successful in various human activity recognition (HAR) applications, but limited work has been done regarding tasks for which it is difficult to collect significant amounts of data, such as manual lifting tasks. In this article, we discuss why traditional methods of data expansion may fail to improve performance on IMU data, and we present a machine learning system capable of detecting lifting action for assessing the risk for back pain using a relatively small amount of data. The proposed models outperform baseline HAR models and function on raw time-series data with minimal preprocessing for efficient real-time application. [Description provided by NIOSH]
• Subjects:
  Ergonomics Occupational Health Safety
• Keywords:
  Author Keywords: Industrial/Organizational/Workplace Safety Back Pain Injury/Illness Prevention Machine Learning Manual Lifting Monitoring Systems Repetitive Work Risk Assessment Signal Detection Task Performance Wearable Systems Wearable Technologies

  More +
• ISSN:
  2168-2291
• 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
• Volume:
  52
• Issue:
  6
• NIOSHTIC Number:
  nn:20066427
• Citation:
  IEEE Trans Hum-Mach Syst 2022 Dec; 52(6):1196-1204
• Contact Point Address:
  Brennan Thomas, Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45219
• Email:
  thoma2bm@mail.uc.edu
• Federal Fiscal Year:
  2023
• Peer Reviewed:
  True
• Source Full Name:
  IEEE Transactions on Human-Machine Systems
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:44a74bf6a5c6cb85e6b577668ba79a5aca298461295992c7b3d6d963096068c7ab3ab9935d0618b547d5547c12208b174641c279dc5a94aa8e8a08bf4a85687f
• Download URL:
  https://stacks.cdc.gov/view/cdc/212857/cdc_212857_DS1.pdf
• File Type:
  [PDF - 2.01 MB ]