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Current Trends in Artificial Intelligence for Recognizing Work Postures to Prevent Work-Related Musculoskeletal

Philippe Gorce1,2, Julien Jacquier-Bret1,2

  • 1University of Toulon, CS 60584, Cedex 9, 83041 Toulon, France.

Bioengineering (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

Artificial intelligence (AI) for recognizing work postures shows promise in preventing work-related musculoskeletal disorders (WMSDs). Deep learning (DL) methods generally outperform machine learning (ML) in accuracy for posture detection in occupational settings.

Keywords:
F1-scoreaccuracydeep learninghuman activity recognitionmachine learningmusculoskeletal disordersoccupational activityprecisionsensitivityspecificity

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Area of Science:

  • Occupational Health and Safety
  • Artificial Intelligence in Healthcare
  • Ergonomics

Background:

  • Work-related musculoskeletal disorders (WMSDs) pose a significant challenge in occupational health.
  • Accurate recognition of working postures is crucial for WMSD prevention.
  • Artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), offers potential solutions for automated posture recognition.

Purpose of the Study:

  • To systematically review and meta-analyze the performance of AI-based work posture recognition systems.
  • To assess the effectiveness of DL and ML methods in identifying postures associated with WMSD risk.
  • To provide insights for developing improved posture detection systems aligned with Industry 5.0 principles.

Main Methods:

  • Systematic literature search across major scientific databases (Google Scholar, IEEE Xplore, PubMed/MedLine, ScienceDirect).
  • Inclusion of peer-reviewed studies analyzing AI (DL/ML) performance metrics (accuracy, precision, specificity, sensitivity, F1-score) for WMSD risk assessment.
  • Risk of bias assessment using the Prediction Model Study Risk of Bias Assessment Tool.

Main Results:

  • 58 studies were selected from 157 unique records.
  • Deep learning (DL) methods demonstrated superior performance compared to machine learning (ML) methods.
  • High accuracy was reported for detecting sitting and standing postures; systems in Manufacturing and Construction were most numerous and effective on average.

Conclusions:

  • AI, especially DL, is effective for recognizing work postures and preventing WMSDs.
  • The variety of methods presents a limitation, highlighting the need for standardization.
  • Future systems should be effective, ergonomic, user-friendly, and human-centered, aligning with Industry 5.0.