Kinetic Chain Analysis of Tennis Stroke Motion Utilizing Wearable Sensors

In tennis training, it is crucial to assist players in comprehending their erroneous movements and enhancing motion efficiency. As the foundation of tennis strokes, kinetic chain theory can significantly improve stroke quality and prevent sports injuries. However, current motion analysis predominantly relies on geometric principles, while kinetic analysis methods have yet to effectively translate kinetic chain theory into an accessible and intuitive motion analysis tool. Additionally, a quantitative metric that can directly elucidate and refine the coordination of players’ movements has not been well-established. Consequently, this research integrates digital human motion data with visual graphs and quantifiable features to scientifically analyze tennis strokes. Initially, human motion data is collected using wearable sensors. Subsequently, motion control theory is examined, and hand contribution velocity (HCV) is defined to measure the impact of each human joint on the overall racket-hand velocity. Following this, the tennis stroke is digitally visualized using the innovative kinetic chain diagram (KCD). Next, contribution features and time delay features are extracted from the KCD, further quantifying the characteristics of the motion for assessment purposes. Finally, 15 tennis players across three skill levels participate in a tennis stroke experiment using an automated ball machine, and their stroke motions are visually analyzed using the proposed methods; the effect of motion and grip style on kinetic chain is also discussed. This study presents an innovative approach that integrates sports motion analysis with information technology, thereby facilitating players’ skill enhancement through a scientific methodology.