Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks
Artykuł w czasopiśmie
MNiSW
100
Lista 2021
| Status: | |
| Autorzy: | Skublewska-Paszkowska Maria, Powroźnik Paweł, Łukasik Edyta |
| Dyscypliny: | |
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| Rok wydania: | 2020 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 21 |
| Wolumen/Tom: | 20 |
| Numer artykułu: | 6094 |
| Strony: | 1 - 12 |
| Web of Science® Times Cited: | 14 |
| Scopus® Cytowania: | 16 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | The research programme titled “Biomechanical parameters of athletes in the individual exercises” based on the analysis of 3D motion data and EMG, realised in the Laboratory of Motion Analysis and Interface Ergonomics was approved by the Commission for Research Ethics, No. 2/2016 dated 8.04.2016. The authors would like to thank Student Sports Club Tennis Academy POL-SART and Sport Club KS-WiNNER for their support. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 27 października 2020 |
| Abstrakty: | angielski |
| 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. |
