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Considering movement competency within physical employment standards.

Daniel P Armstrong1, Gwyneth B Ross2, Ryan B Graham2

  • 1Department of Kinesiology, University of Waterloo, Waterloo, Canada.

Work (Reading, Mass.)
|July 9, 2019
PubMed
Summary

Artificial intelligence (AI) can assess movement competency in physical employment standards (PES) by analyzing motion data. This approach helps identify individuals at higher risk for musculoskeletal disorders (MSD), improving injury prevention.

Keywords:
Artificial intelligencebiomechanical exposuredemand-capacity-competencyergonomicsmachine learning

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Area of Science:

  • Biomechanical analysis
  • Artificial Intelligence (AI)
  • Occupational Health

Background:

  • Physical employment standards (PES) ensure job-specific physical capacity.
  • Movement competency is crucial for injury risk and safety in physically demanding roles.
  • Integrating movement strategy assessment into PES can enhance safety.

Purpose of the Study:

  • To demonstrate the effectiveness of AI in detecting movement strategy risks within PES.
  • To utilize AI for analyzing biomechanical data related to physical tasks.
  • To classify movement patterns associated with varying levels of spinal loading.

Main Methods:

  • Biomechanical analysis calculated spinal loading during a backboard lifting task.
  • Lifts were categorized as 'low' or 'high' exposure based on postural and moment loading.
  • An AI model, using principal component and linear discriminant analyses, was trained for classification.

Main Results:

  • The AI model achieved over 85% accuracy in classifying lifts.
  • Classification was based solely on motion data input.
  • Demonstrated AI's capability to differentiate between low and high spinal loading patterns.

Conclusions:

  • AI can effectively assess movement competency as part of PES.
  • This AI-driven approach can enhance PES utility for musculoskeletal disorder (MSD) prevention.
  • Proactively identifying at-risk candidates based on movement competency is feasible.