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Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks.

Jun Zhang^1,2, Yuanshi Ren³, Liyue Lin⁴

¹School of Exercise and Health, Shanghai University of Sport, Shanghai, 200438, China.

Scientific Reports

|February 12, 2024

Summary

This study introduces a novel motion recognition algorithm using only table tennis paddle trajectories. This method enhances recognition speed for table tennis skills.

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Area of Science:

Sports Science
Computer Vision
Robotics

Background:

Action and motion recognition are vital in diverse fields like smart homes, gaming, and biomechanical analysis.
Existing methods often rely on complex multijoint data for action identification.
Table tennis skill analysis benefits from efficient and accurate motion recognition.

Purpose of the Study:

To develop a motion recognition algorithm specifically for table tennis skills.
To analyze the characteristics of table tennis paddle trajectories.
To create a new dataset, TTMD6, for table tennis motion data.

Main Methods:

Collected and analyzed table tennis skill motion data.
Developed a novel algorithm focusing solely on paddle trajectories.
Created the TTMD6 dataset comprising table tennis motion data.

Main Results:

Successfully analyzed key characteristics of table tennis paddle trajectories.
The proposed algorithm accurately recognizes table tennis skill motions using only paddle trajectory data.
Achieved accelerated motion recognition speeds compared to multijoint data methods.

Conclusions:

Paddle trajectories are a feasible and efficient feature for recognizing table tennis skill motions.
The TTMD6 dataset and the proposed algorithm offer advancements in table tennis motion analysis.
This approach significantly enhances the speed of motion recognition in sports applications.