Reliability Modeling of Shoulder Fatigue and Recovery for Warehouse Operators Performing Dynamic Tasks

Details

• Personal Author:
  Cavuoto LA
• Description:
  Many occupational environments require labor intensive activities, which could result in fatigue and injuries and cause decreased work performance. Recently, the breakthroughs of Industry 4.0 have allowed for monitoring worker conditions through the use of wearable sensors. A systematic literature review conducted as part of this project identified that previous models of worker fatigue lack the consideration of several important aspects of upper limb fatigue analysis, such as the full use of motion data in a time series manner, consideration of the heterogeneity of workers and measurement devices, and the difference between simulated and actual work environment. In this project, we conducted a laboratory experiment and collected data from individuals performing simulated ordering picking, moving bottles from shoulder height to waist level, for three hours at four different pace and bottle weight combinations. During the three-hour task, participants completed three 45-minute work periods separated by 15-minute rest periods. The main findings include: 1) For the first 45-minute work period, the average correlations between perceived exertion and strength change were equivalent across conditions. This extends previous findings from controlled isometric or isokinetic tasks to this less controlled dynamic one. 2) Participants returned to approx. 90% of their baseline strength following the first rest period. The starting perceived exertion level did not reset to baseline following rest. After this, correlations were weaker when re-engaging in the work task. Thus, starting fatigue levels should be accounted for when considering the expected relationship between perceived exertion and strength change. 3) A functional approach was effective in detecting the impact of task factors, pace and load, on fatigue indicators. We also explored the use of the functional relationships of kinematic features and fatigue outcomes for more accurate fatigue identification. These methods support our understanding of the time course of fatigue development and recovery for the upper extremity. 4) The use of wearable sensors was investigated for automating ergonomic risk assessment. We showed that, with a motion sensor worn on the wrist, we could achieve >90% accuracy for task identification, approx. 7% error rate for estimating task duration, and approx. 7% error for counting task repetitions. This supports the utility of the framework for estimating important risk factors, particularly when accompanied with knowledge of task loads. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Ergonomics Fatigue Musculoskeletal System Occupational Health Safety
• Keywords:
  Biomechanical Modeling Biomechanics Sensors Upper Extremities Warehousing Wearable Technologies
• Series:
  Grant Final Reports
• Publisher:
  National Institute for Occupational Safety and Health
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Final Grant Report
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  OEP - Office of Extramural Programs
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-16
• NIOSHTIC Number:
  nn:20069545
• Citation:
  Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, R21-OH-011749, 2023 Dec; :1-16
• Email:
  loracavu@buffalo.edu
• Federal Fiscal Year:
  2024
• NORA Priority Area:
  Transportation, Warehousing and Utilities
• Performing Organization:
  State University of New York at Buffalo
• Peer Reviewed:
  False
• Start Date:
  20200930
• Source Full Name:
  National Institute for Occupational Safety and Health
• End Date:
  20220929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0672cc4f7a3b4acb0935a11921bcb8a01c568b90d0a349bb281d024da9da62252115ffd319507d66fc27a5af3e4b32aac3ff867b7ccf60bc4aa16bb8e12c35a2
• Download URL:
  https://stacks.cdc.gov/view/cdc/219237/cdc_219237_DS1.pdf
• File Type:
  [PDF - 1.79 MB ]