Using Artificial intelligence to assist sports training is a hallmark of the information age. Table tennis, a popular sport in China and worldwide, can also benefit from the latest technological advancements in learning and training. Objective: This study aims to quantitatively evaluate the effectiveness of forehand strokes of different-level table tennis players using dynamic time-warping (DTW) algorithms in AI. Twenty-five beginner players (no prior Table Tennis experience, Male:22 Female:3, Age:18-28) and twenty-five intermediate players (At least 2 years of Table Tennis experience, Male:22 Female:3, Age:18-28) were recruited based on convenience sampling. After obtaining consent, they received 2 hours of basic training on the forehand stroke. Then, their hitting poses are recorded with the system for a 15-second video stream. A dynamic time-wrapping (DTW) algorithm was applied to recognize individual strokes and calculate the difference between the elbow angles of beginner players’ strokes and intermediate players’ strokes; the DTW value was the square of the difference between the two angles. At the same time, the recording is sent to two professional table tennis coaches to evaluate the hitting position with a composite score of 0-100, where 100 stands for most effective and 0 stands for least effective. And then compare their hitting poster with professional athletes. The evaluation criteria included good timing, speed, landing point, and movement rationalization. A study of 25 beginners and 25 intermediate table tennis players analyzed stroke effectiveness via two coach evaluations (0-100) and normalized DTW values. Coaches demonstrated high inter-rater reliability (beginners: p=0.48; intermediates: p=0.72) and significantly distinguished skill levels (p < 0.001). Beginners exhibited higher DTW values (48.14±10.81 vs. 40.17±7.34, p=0.004) with a large Cohen’s d 0.86, indicating a substantial difference. These findings confirm DTW’s capacity to statistically differentiate skill levels, aligning with expert assessments. Thus, normalized DTW effectively correlates with human-evaluated stroke quality, validating its utility as an objective metric for distinguishing player proficiency. Discussion/Conclusion: With a dynamic time-wrapping algorithm, the system could differentiate beginner and intermediate players. One area for improvement lies in refining the DTW algorithm to account for a broader range of individual factors such as body height, arm span, and player-specific biomechanics. Also, the DTW score can potentially be used to rate performance in terms of a player’s posture, grip, swing mechanics, and other racket sports.
He et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: