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Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base
Wenlong Ma1, Yang Liu2, Qing Yi3
1School of Physical Education, Shanghai University of Sport, Shanghai, China.
Plos One
|February 13, 2025
Summary
This study introduces an AI table tennis coaching system that uses a Multimodal Large Language Model to precisely guide beginners. The system accurately identifies common errors, enhancing training efficiency and promoting the sport.
Area of Science:
- Sports Science
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Table tennis is a globally popular sport promoting physical and mental well-being.
- Beginner table tennis players often struggle with identifying and correcting common errors.
Purpose of the Study:
- To develop an AI table tennis coaching system for beginners.
- To provide precise training guidance and match strategies.
- To leverage Multimodal Large Language Models (MLLMs) and a dedicated knowledge base.
Main Methods:
- Utilized visual recognition and motion capture technologies.
- Employed advanced MLLMs integrated with a comprehensive table tennis knowledge base.
- Developed a system for accurate identification of beginner errors.
Main Results:
- The AI system achieved high accuracy in identifying arm-related errors (73%) and racket-related errors (82%).
- Demonstrated effectiveness in providing targeted training guidance for common mistakes.
Conclusions:
- The AI table tennis coaching system is cost-effective and easy to deploy.
- Offers significant potential to improve training efficiency and athlete performance.
- Future work includes enhancing footwork recognition and catering to advanced players.
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