We introduce TTSwing, a novel dataset designed to analyze table tennis swings. The dataset was collected using custom racket grips embedded with 9-axis motion sensors, which provide precise kinematic data on swings. In addition, we provide anonymized demographic data for players. The dataset was collected from 93 participants, all of whom are elite table tennis players from Taiwan. We detail the data collection and annotation procedures. These data are expected to improve the understanding of player performance and facilitate the development of tailored training programs and biomechanical analyses, offering practical benefits to both athletes and coaches. TTSwing has excellent potential to facilitate innovative research in table tennis analysis and is a valuable resource for the scientific community. We release the dataset and the experimental codes for reproducibility.
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http://dx.doi.org/10.1038/s41597-025-04680-y | DOI Listing |
BMC Sports Sci Med Rehabil
March 2025
School of Physical Education, Soochow University, 50 Donghuan Road, Suzhou, Jiangsu Province, 215021, China.
Background: Game performance analysis has been playing a significant role in sports events which has reached an international consensus. In the field of technical and tactical analysis of table tennis, many studies conducted the segmented evaluation of players based on the phased-theory. The present study proposed the concepts of "competitive technical and tactical performance" of elite table tennis players.
View Article and Find Full Text PDFFront Sports Act Living
February 2025
Faculty of Health Sciences, Institute of Physiotherapy and Sport Science, University of Pécs, Pécs, Hungary.
Introduction: One of the most effective techniques is "stress inoculation" training (SIT), which is increasingly utilized to reduce anxiety and enhance athletic performance. The aim of our research was to investigate the extent to which the stress situation we created in virtual reality evokes psychological responses in athletes, compared to the responses they experience during a competitive match.
Methods: The sample consisted of 24 female athletes with an average age of 18.
Sci Rep
March 2025
College of Sport and Health, Shandong Sport University, 10600 Century Avenue, Licheng District, Jinan City, 250100, Shandong Province, China.
Long short-term memory (LSTM) networks are widely used in biomechanical data analysis but have the significant limitations in interpretability and decision transparency. Combining graph neural networks (GNN) with gate recurrent units (GRU) may offer a better solution. This study proposes and validates a hybrid GNN-GRU model for predicting baseball pitching speed and enhancing its interpretability using layer-wise relevance propagation (LRP).
View Article and Find Full Text PDFSci Data
February 2025
Department of Sport Performance, National Taiwan University of Sport, Taichung, 404401, Taiwan.
We introduce TTSwing, a novel dataset designed to analyze table tennis swings. The dataset was collected using custom racket grips embedded with 9-axis motion sensors, which provide precise kinematic data on swings. In addition, we provide anonymized demographic data for players.
View Article and Find Full Text PDFJ Multidiscip Healthc
February 2025
Maharishi Markandeshwar Institute of Physiotherapy and Rehabilitation, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India.
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