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Dynamic Golf Swing Analysis Framework Based on Efficient Similarity Assessment.

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A new Dynamic Motion Similarity Measurement (DMSM) framework enhances golf swing analysis by evaluating joint trajectories across seven phases, improving accuracy and biomechanical insights over traditional methods.

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Area of Science:

  • Sports Science
  • Biomechanics
  • Computer Vision

Background:

  • Image-based pose estimation offers cost-effective motion analysis.
  • Conventional methods struggle with golf swing temporal alignment and perceptual accuracy.
  • Need for dynamic, phase-specific analysis in golf swing motion capture.

Purpose of the Study:

  • Introduce a Dynamic Motion Similarity Measurement (DMSM) framework for golf swing analysis.
  • Improve perceptual consistency and biomechanical interpretability of motion capture data.
  • Develop a practical foundation for AI-based golf swing analysis and real-time feedback.

Main Methods:

  • Segmenting golf swings into seven canonical phases.
  • Evaluating dynamic trajectories of joint keypoints within each phase.
  • Integrating continuous motion, normalizing coordinates, interpolating data, and using numerical integration for path differences.

Main Results:

  • DMSM shows stronger discrimination between same- and different-player swing pairs.
  • Achieved significant biomechanical distinction in spine-angle trajectories (Δ = 38.68, p < 0.05).
  • Coach evaluations confirmed perceptual alignment with the new framework.

Conclusions:

  • DMSM provides more reliable phase-wise feedback and biomechanically interpretable motion analysis.
  • The framework is computationally feasible with minor overhead (≈169 ms).
  • Offers a practical foundation for AI-driven sports training and real-time feedback systems.