An Analysis of Movement Variability in Upper Body Postures and a Validation Study for a Motion Sensing Garment

Details

• Personal Author:
  Allen KM
• Description:
  Upper extremity musculoskeletal disorders are an all-too-common injury within the workplace. These injuries can cost upwards of $50 billion annually when considering time away from work, lost productivity, health care costs, and worker's compensation costs. Musculoskeletal disorders result from high forces, repetitive motions, and awkward postures. Inter-subject variability also contributes to the risk of injury, as different individuals complete the same tasks in different ways. Monitoring posture in the workplace is challenging, but wearable sensor technology like the Motion Sensing Garment (MSG) evaluated in this work could provide that capability. This research analyzes movement variability within a population across various tasks and evaluates the MSG as an ergonomic monitoring tool. A motion capture study was designed to characterize individuals' movements in a range of tasks, and analysis shows that individual execution of tasks is variable and can result in different categories of risk for injury according to the Rapid Upper Limb Assessment (RULA). The movements that involve a greater number of joints and cross more than one plane of movement tend to result in greater kinematic variability and thus lend themselves to larger ranges of risk. This demonstrates that designing a task for the average individual is not always enough to protect a whole population of workers from musculoskeletal injury. The MSG is a wearable garment instrumented with seven IMUs to track upper-body segments. The use of the MSG within the workplace to monitor ergonomic posture could significantly impact the ability to identify and correct ergonomically hazardous tasks. Kinematic data from the MSG corresponded well with the optical motion capture data for some movements, primarily single-joint movements. However, errors with the MSG algorithm led to large differences between the MSG joint angles and the optical motion capture joint angles for more complex movements. These errors prevented wide-scale validation. Continued development with the proprietary algorithm could resolve these differences. This work highlights the importance of considering ergonomic variability within a working population and evaluates the MSG as a potential solution for hazard recognition and evaluation of individual ergonomic posture during occupational tasks. [Description provided by NIOSH]
• Subjects:
  Ergonomics Musculoskeletal Diseases Musculoskeletal System Occupational Health Safety
• Keywords:
  Biomechanical Engineering MSD Musculoskeletal Disorders Posture Sensors Upper Extremities Wearable Technologies
• Publisher:
  University of Utah
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Thesis
• Place as Subject:
  OSHA Region 8 ; Utah
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-71
• NIOSHTIC Number:
  nn:20068694
• Citation:
  Salt Lake City, UT: University of Utah, 2023 May; :1-71
• Federal Fiscal Year:
  2023
• Performing Organization:
  University of Utah
• Peer Reviewed:
  False
• Start Date:
  20050701
• Source Full Name:
  An analysis of movement variability in upper body postures and a validation study for a motion sensing garment
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:503f54692cf7bcdfd755755ecc2a7c21f67d698a12c10efa6bc366c95d176b896d6e74078b60d944f49cd49807730aa67170db88599288ee38eaca65704a6ac1
• Download URL:
  https://stacks.cdc.gov/view/cdc/207284/cdc_207284_DS1.pdf
• File Type:
  [PDF - 3.35 MB ]