Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base
The system operates at low costs, is easy to deploy, and offers a high cost-performance ratio, providing effective technological support for table tennis teaching and training. The AI table tennis coaching system is expected to play a significant role in enhancing training efficiency, promoting athlete skill improvement, and popularizing the sport. Future research will focus on improving the accuracy of footwork recognition in AI table tennis coaching systems and expanding their capability to provide training guidance for high-level athletes, thereby promoting the overall advancement of table tennis.
Authors
- Xutao Liu (ORCID: https://orcid.org/0000-0003-1665-3893)
- Qing Yi (ORCID: https://orcid.org/0000-0003-1529-3398)
- Huan Liu (ORCID: https://orcid.org/0000-0003-2937-0198)
- Yang Liu (ORCID: https://orcid.org/0000-0003-4468-3926)
- Rongzhi Li
- Wei Xing (ORCID: https://orcid.org/0009-0003-6925-4500)
- Rui Zhao (ORCID: https://orcid.org/0009-0005-3873-168X)
- Wenlong Ma
Institutions
- Tongji University (CN)
- Shanghai University of Sport (CN)
- Beijing Normal University (CN)
- University of Malaya (MY)
- Jiangsu University of Science and Technology (CN)
- Wuhan Business University (CN)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1371/journal.pone.0317839
- Citations
- 8
- Primary Topic
- Human Pose and Action Recognition
- Type
- article
- Field-Weighted Citation Impact
- 8.93