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.

本研究提出了一种通过生成个性化运动指南来提高羽毛球球员击球质量的框架，利用多模态可穿戴数据集。这些指南基于反事实算法，旨在减小初学者与专家之间的表现差距。我们的方法通过可视化数据提供关节级别的指导，以帮助球员改善动作，而无需专家知识。通过度量指标对该方法与传统算法进行了评估，包括算术度量和动作特定度量，以评估其有效性、接近性和合理性。我们的评估表明，所提出的框架能够生成保持原始动作精髓的动作，同时提高击球质量，相较于直接复制专家动作，提供更贴近的指导。结果凸显了我们的方法在通过为羽毛球击球的任意输入样本生成反事实运动指导的个性化体育运动指南方面的潜力。