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Vision-Based Computing Pipeline for Recognizing Hand Grip-Types During Tool Handling.

Francis Baek1, Daeho Kim2, Julia Penfield3

  • 1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA.

IISE Transactions on Occupational Ergonomics and Human Factors
|August 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a computer vision system to identify hand grips during tool use, aiding in the prevention and diagnosis of hand musculoskeletal disorders (MSDs) through noninvasive assessments.

Keywords:
3D hand pose estimationHand grip classificationmusculoskeletal disorders

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Area of Science:

  • Occupational Health
  • Computer Vision
  • Ergonomics

Background:

  • Hand tool use is a significant risk factor for musculoskeletal disorders (MSDs).
  • Current ergonomic assessments often require extensive training and can be invasive.
  • Accurate, continuous monitoring of hand-tool interactions is needed for MSD prevention.

Purpose of the Study:

  • To develop and validate a computer vision pipeline for recognizing hand grip types during tool handling.
  • To provide a noninvasive, continuous method for assessing hand ergonomics.
  • To support early detection and management of hand-related MSDs.

Main Methods:

  • Utilized a monocular red, green, and blue (RGB) camera for image acquisition.
  • Developed a computer vision algorithm to classify different hand grip types.
  • Focused on continuous, noninvasive data collection during simulated occupational tasks.

Main Results:

  • The pipeline accurately recognizes various hand grip types from RGB images.
  • The system provides continuous data on grip type, duration, and repetition.
  • Demonstrated potential for integration with broader ergonomics assessment tools.

Conclusions:

  • The proposed computer vision pipeline offers an accessible and effective method for assessing hand ergonomics.
  • This technology can significantly aid in the prevention, early diagnosis, and treatment of hand MSDs in occupational settings.
  • The system's versatility allows for comprehensive MSD risk analysis across multiple body parts.