TTSWING: a Dataset for Table Tennis Swing Analysis

Che-Yu Chou, Zheng-Hao Chen, Yung-Hoh Sheu, Hung-Hsuan Chen, Sheng K. Wu
6/30/2023
cs.LGcs.AI

Official code: DEPhantom/TTSWING (8 stars, Apache-2.0, last commit 2025-02-06). 1 more implementation below.

Abstract

We introduce TTSWING, a novel dataset designed for table tennis swing analysis. This dataset comprises comprehensive swing information obtained through 9-axis sensors integrated into custom-made racket grips, accompanied by anonymized demographic data of the players. We detail the data collection and annotation procedures. Furthermore, we conduct pilot studies utilizing diverse machine learning models for swing analysis. TTSWING holds tremendous potential to facilitate innovative research in table tennis analysis and is a valuable resource for the scientific community. We release the dataset and experimental codes at https://github.com/DEPhantom/TTSWING.

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Code Implementations(2)

DEPhantom/TTSWINGOfficial100%
Apache-2.0
80PythonJun 17, 20231 years agoApache-2.0

Machine learning-based analysis of table tennis swing motion data to classify player health status and detect abnormal performance patterns. Built using supervised and unsupervised models on the TTSWING dataset from arXiv:2306.17550.

00Jupyter NotebookApr 18, 20251 years ago

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Cite this paper

@article{chou2023ttswing,
  title  = {TTSWING: a Dataset for Table Tennis Swing Analysis},
  author = {Che-Yu Chou and Zheng-Hao Chen and Yung-Hoh Sheu and Hung-Hsuan Chen and Sheng K. Wu},
  year   = {2023},
  eprint = {2306.17550},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.17550v1}
}

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