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Reinforcement Learning-Based Golf Swing Correction Framework Incorporating Temporal Rhythm and Kinematic Stability.

Dong-Jun Lee1, Young-Been Noh1, Jeongeun Byun2

  • 1Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Incheon 22012, Republic of Korea.

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Summary

This study introduces a reinforcement learning framework for golf swing correction, focusing on temporal rhythm and stability. The AI-generated corrections are smoother and more coordinated than previous methods, enhancing dynamic joint movements.

Keywords:
Proximal Policy OptimizationVelocity-DTWgolf swing correctionpose-based motion analysisreinforcement learningtemporal rhythm

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Area of Science:

  • Sports Biomechanics
  • Artificial Intelligence
  • Robotics

Background:

  • Existing golf swing analysis often misses crucial temporal dynamics like rhythm and velocity transitions.
  • Frame-wise pose similarity fails to capture the coordinated, whole-body motion essential for effective swing correction.

Purpose of the Study:

  • To develop a reinforcement learning (RL) framework for generating frame-level golf swing corrections.
  • To integrate temporal rhythm and biomechanical stability into automated swing analysis and correction.

Main Methods:

  • Formulated golf swing correction as a sequential decision-making problem optimized using Proximal Policy Optimization (PPO).
  • Designed a multi-term reward function incorporating pose accuracy, incremental improvement, stability, and rhythm (Velocity-DTW).

Main Results:

  • The RL-based method produced smoother, temporally coherent corrections compared to static pose baselines.
  • Rhythm-aware rewards significantly improved dynamic joint motion (wrists, shoulders) while maintaining lower-body stability.
  • Corrected trajectories matched expert patterns in spatial alignment and timing.

Conclusions:

  • Explicitly incorporating temporal rhythm in RL is crucial for realistic and effective golf swing correction.
  • The proposed framework offers a foundation for automated, expert-level coaching systems in dynamic sports.
  • This approach enhances biomechanical analysis by considering the full temporal sequence of motion.