Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks
Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training ses...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/21/6094 |
| _version_ | 1827703511410802688 |
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| author | Maria Skublewska-Paszkowska Pawel Powroznik Edyta Lukasik |
| author_facet | Maria Skublewska-Paszkowska Pawel Powroznik Edyta Lukasik |
| author_sort | Maria Skublewska-Paszkowska |
| collection | DOAJ |
| description | Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input. |
| first_indexed | 2024-03-10T15:19:06Z |
| format | Article |
| id | doaj.art-bd5da3bdffd7475abd1179e23b81b38e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:19:06Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bd5da3bdffd7475abd1179e23b81b38e2023-11-20T18:39:03ZengMDPI AGSensors1424-82202020-10-012021609410.3390/s20216094Learning Three Dimensional Tennis Shots Using Graph Convolutional NetworksMaria Skublewska-Paszkowska0Pawel Powroznik1Edyta Lukasik2Department of Computer Science, Lublin University of Technology, 20-618 Lublin, PolandDepartment of Computer Science, Lublin University of Technology, 20-618 Lublin, PolandDepartment of Computer Science, Lublin University of Technology, 20-618 Lublin, PolandHuman movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.https://www.mdpi.com/1424-8220/20/21/6094tennis movement recognitionST-GCNfuzzy data |
| spellingShingle | Maria Skublewska-Paszkowska Pawel Powroznik Edyta Lukasik Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks Sensors tennis movement recognition ST-GCN fuzzy data |
| title | Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks |
| title_full | Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks |
| title_fullStr | Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks |
| title_full_unstemmed | Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks |
| title_short | Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks |
| title_sort | learning three dimensional tennis shots using graph convolutional networks |
| topic | tennis movement recognition ST-GCN fuzzy data |
| url | https://www.mdpi.com/1424-8220/20/21/6094 |
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