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  11. Table Tennis Coaching System Based On A Multimodal Large Language Model With A Table Tennis Knowledge Base

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

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View abstract on PubMed

Summary
This summary is machine-generated.

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.

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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.