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April 30, 2026Eng—Advances in Engineering0 citationsOpen Access

AI Computer Vision Assesses Ergonomic Risks in Metal Polishing Workers

AI-Powered Computer Vision for Ergonomic Risk Assessment and Musculoskeletal Symptom Prevalence in Industrial Metal Polishing Operators

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Authors

JAJoel AlvesTLTânia M. LimaPGPedro D. Gaspar

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Overview

Demonstrates high musculoskeletal symptom prevalence in metal polishing workers, indicating ergonomic risks require targeted interventions.

Key Points

  • To validate an ergonomic assessment methodology for musculoskeletal risk in industrial polishing operators using AI.
  • Surveyed 41 workers with the Nordic Musculoskeletal Questionnaire
  • Assessed 27 workers using the REBA method powered by AI-based computer vision
  • Analyzed correlations between sociodemographic, health variables, and musculoskeletal risks.
  • Highest symptom prevalence observed in neck (82.9%), shoulders (70.8%), lower back (68.3%), and wrists/hands (65.9%)
  • Identified moderate (70.3%), high (26.0%), and very high (3.7%) WRMSD risks for the upper arms, neck, and trunk
  • Significant associations between age, gender, health perception, and musculoskeletal risks.

Cite This Study

Alves et al. (2026) studied this question.

synapsesocial.com/papers/69f2a42a8c0f03fd677633a8https://doi.org/10.3390/eng7050204
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