Counterfactual Explanation-Based Badminton Motion Guidance Generation Using Wearable Sensors
This provides personalized sports motion guidance for badminton players to reduce the novice-expert performance gap, though it appears incremental as it builds on existing counterfactual methods in a specific domain.
The study tackled the problem of improving badminton stroke quality by developing a framework that generates personalized motion guides using counterfactual algorithms and wearable sensor data, demonstrating that it produces motions maintaining original movement essence while enhancing stroke quality with closer guidance than expert motion replication.
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.