Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles
Racket sports such as table tennis involve a wide range of three-dimensional complex spatial movements of the human body and the racket. Novice players might benefit from the evaluation of the motion profile of the racket to facilitate better adoption of more expert movement. Computer-based evaluati...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2024-01-01
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| Series: | Computing Open |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370124500053 |
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| author | Zhiqing Zhang Parvathi Nathan Pazhaya Veedu Benjamin Halkon Siaw Meng Chou |
| author_facet | Zhiqing Zhang Parvathi Nathan Pazhaya Veedu Benjamin Halkon Siaw Meng Chou |
| author_sort | Zhiqing Zhang |
| collection | DOAJ |
| description | Racket sports such as table tennis involve a wide range of three-dimensional complex spatial movements of the human body and the racket. Novice players might benefit from the evaluation of the motion profile of the racket to facilitate better adoption of more expert movement. Computer-based evaluation of such novice vs. expert play behavior characteristics includes reducing the required multiple human interactions and easy applicability for subsequent automation to accurately differentiate the motion profile of a novice player from that of an expert. This study has, for the first time, applied the widely used support vector machine (SVM) classification technique for the development of a table tennis player movement evaluation model. The model was trained using an existing dataset of displacements and velocities from various important anatomical landmarks across the body and points on the racket. These were obtained and evaluated for table tennis forehand strokes for two subgroups of expert and novice ability levels, respectively. Different combinations of variables were selected for model input from the same dataset with the outcomes being noted for each. The resulting SVM classification model exhibited good/noteworthy performance ([Formula: see text]% accuracy) in distinguishing racket motion between expert and novice players. |
| format | Article |
| id | doaj-art-7d8def907ee84a22af91d63de22b13c5 |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Computing Open |
| spelling | doaj-art-7d8def907ee84a22af91d63de22b13c52025-02-04T03:24:11ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370124500053Application of Support Vector Machine in the Evaluation of Table Tennis Motion ProfilesZhiqing Zhang0Parvathi Nathan Pazhaya Veedu1Benjamin Halkon2Siaw Meng Chou3School of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeSchool of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeCentre for Audio, Acoustics and Vibration, Faculty of Engineering and IT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, AustraliaSchool of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeRacket sports such as table tennis involve a wide range of three-dimensional complex spatial movements of the human body and the racket. Novice players might benefit from the evaluation of the motion profile of the racket to facilitate better adoption of more expert movement. Computer-based evaluation of such novice vs. expert play behavior characteristics includes reducing the required multiple human interactions and easy applicability for subsequent automation to accurately differentiate the motion profile of a novice player from that of an expert. This study has, for the first time, applied the widely used support vector machine (SVM) classification technique for the development of a table tennis player movement evaluation model. The model was trained using an existing dataset of displacements and velocities from various important anatomical landmarks across the body and points on the racket. These were obtained and evaluated for table tennis forehand strokes for two subgroups of expert and novice ability levels, respectively. Different combinations of variables were selected for model input from the same dataset with the outcomes being noted for each. The resulting SVM classification model exhibited good/noteworthy performance ([Formula: see text]% accuracy) in distinguishing racket motion between expert and novice players.https://www.worldscientific.com/doi/10.1142/S2972370124500053Support vector machinetable tennisperformanceevaluationcross-validation |
| spellingShingle | Zhiqing Zhang Parvathi Nathan Pazhaya Veedu Benjamin Halkon Siaw Meng Chou Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles Computing Open Support vector machine table tennis performance evaluation cross-validation |
| title | Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles |
| title_full | Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles |
| title_fullStr | Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles |
| title_full_unstemmed | Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles |
| title_short | Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles |
| title_sort | application of support vector machine in the evaluation of table tennis motion profiles |
| topic | Support vector machine table tennis performance evaluation cross-validation |
| url | https://www.worldscientific.com/doi/10.1142/S2972370124500053 |
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