  This study proposes a framework for enhancing the stroke quality of badminton
players by generating personalized motion guides, utilizing a multimodal
wearable dataset. These guides are based on counterfactual algorithms and aim
to reduce the performance gap between novice and expert players. Our approach
provides joint-level guidance through visualizable data to assist players in
improving their movements without requiring expert knowledge. The method was
evaluated against a traditional algorithm using metrics to assess validity,
proximity, and plausibility, including arithmetic measures and motion-specific
evaluation metrics. Our evaluation demonstrates that the proposed framework can
generate motions that maintain the essence of original movements while
enhancing stroke quality, providing closer guidance than direct expert motion
replication. The results highlight the potential of our approach for creating
personalized sports motion guides by generating counterfactual motion guidance
for arbitrary input motion samples of badminton strokes.
