Abstract
The adoption of artificial intelligence (AI) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision and machine learning for real-time performance analysis. The system integrates YOLOv5 for high-precision ball detection (98% accuracy) and MediaPipe for athlete posture evaluation. A dynamic time-wrapping algorithm further assesses stroke effectiveness, demonstrating statistically significant discrimination between beginner and intermediate players (p = 0.004 and Cohen’s d = 0.86) in a cohort of 50 participants. By automating feedback and reducing reliance on expert observation, this system offers a scalable tool for coaching, self-training, and sports analysis. Its modular design also allows adaptation to other racket sports, highlighting broader utility in athletic training and entertainment applications. Copyright © 2025 by the authors.
| Original language | English |
|---|---|
| Article number | 5370 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 15 |
| Early online date | May 2025 |
| DOIs | |
| Publication status | Published - 2025 |
Citation
He, Z., Yang, Z., Xu, J., Chen, H., Li, X., Wang, A., Yang, J., Chow, G. C.-C., & Chen, X. (2025). Real-time accurate determination of table tennis ball and evaluation of player stroke effectiveness with computer vision-based deep learning. Applied Sciences, 15, Article 5370. https://doi.org/10.3390/app15105370Keywords
- Technology in sports
- AI and machine learning
- Table tennis training
- Dynamic time wrapping
- Assessment
