Counterfactual Explanation-Based Badminton Motion Guidance Generation Using Wearable Sensors

TL;DR

This paper introduces a novel framework that uses wearable sensors and counterfactual algorithms to generate personalized badminton motion guides, helping players improve stroke quality by providing visualized joint-level guidance.

Contribution

The study presents a new approach combining wearable sensor data and counterfactual algorithms to generate personalized, visualizable badminton motion guidance without requiring expert intervention.

Findings

Generated motions maintain original movement essence

Guidance improves stroke quality compared to baseline

Framework effectively personalizes sports motion guidance

Abstract

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.