Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task Using Posture Kinematics

Details

• Personal Author:
  D'Souza C ; Lim S
• Description:
  This study explored the use of body posture kinematics derived from wearable inertial sensors to estimate force exertion levels in a two-handed isometric pushing and pulling task. A prediction model was developed grounded on the hypothesis that body postures predictably change depending on the magnitude of the exerted force. Five body postural angles, viz., torso flexion, pelvis flexion, lumbar flexion, hip flexion, and upper arm inclination, collected from 15 male participants performing simulated isometric pushing and pulling tasks in the laboratory were used as predictor variables in a statistical model to estimate handle height (shoulder vs. hip) and force intensity level (low vs. high). Individual anthropometric and strength measurements were also included as predictors. A Random Forest algorithm implemented in a two-stage hierarchy correctly classified 77.2% of the handle height and force intensity levels. Results represent early work in coupling unobtrusive, wearable instrumentation with statistical learning techniques to model occupational activities and exposures to biomechanical risk factors in situ. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Environmental Exposure Laboratories Men Musculoskeletal System Occupational Health Risk Assessment Risk Factors Safety
• Keywords:
  Anthropometry Biomechanics Body Mechanics Exposure Levels Force Humans Laboratory Testing Models Physiological Function Posture Risk Factors Sensors Task Performance

  More +
• ISBN:
  9780945289531
• ISSN:
  1541-9312
• Publisher:
  Human Factors and Ergonomics Society
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Proceedings
• Place as Subject:
  Michigan ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  61
• Issue:
  1
• NIOSHTIC Number:
  nn:20050775
• Citation:
  Proceedings of the Human Factors and Ergonomics Society 61st Annual Meeting, October 9-13, 2017, Austin, Texas. Santa Monica, CA: Human Factors and Ergonomics Society, 2017 Sep; 61(1):1031-1035
• Federal Fiscal Year:
  2017
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Proceedings of the Human Factors and Ergonomics Society 61st Annual Meeting, October 9-13, 2017, Austin, Texas
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:eaf2c9b60d169c15fddb69952d9209d06cec7ded68a4944c19537da7092f3008c3e7e1e27546b947898ed91bb5966a1d5b87f8f2730039f72252f4e02b69bd1a
• Download URL:
  https://stacks.cdc.gov/view/cdc/210995/cdc_210995_DS1.pdf
• File Type:
  [PDF - 1.34 MB ]