Design of tennis auxiliary teaching system based on reinforcement learning and multi-feature fusion

To accurately identify and evaluate tennis movements, a tennis auxiliary teaching system based on reinforcement learning and multi-feature fusion was designed by combining deep learning methods with tennis-related knowledge to recognize and evaluate tennis movements accurately. The algorithm first e...

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Veröffentlicht in:PeerJ. Computer science Jg. 11; S. e3188
Hauptverfasser: Zhang, Shiquan, Gan, Chaohong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States PeerJ. Ltd 09.09.2025
PeerJ Inc
Schlagworte:
Algorithms
Artificial neural networks
Education
Human pose recognition algorithm
Machine learning
Methods
Multi feature fusion
Reinforcement learning
Tennis
Multi feature fusion
Human pose recognition algorithm
Artificial neural networks
Reinforcement learning
ISSN:2376-5992, 2376-5992
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Zusammenfassung:To accurately identify and evaluate tennis movements, a tennis auxiliary teaching system based on reinforcement learning and multi-feature fusion was designed by combining deep learning methods with tennis-related knowledge to recognize and evaluate tennis movements accurately. The algorithm first extracts human skeletal joint points from a video sequence using a human pose-recognition algorithm. Reinforcement learning is then used to extract and optimize the keyframes. Second, genetic algorithms were used to fuse the different features. The results demonstrate that the proposed tennis action recognition method achieves a classification accuracy of 98.45% for four types of tennis subactions. Its generalization ability is greater than that of graph convolutional network-based techniques, such as AGCN and ST-GCN. Lastly, following action categorization, the suggested scoring method based on dynamic temporal warping may deliver accurate and real-time assessment ratings for corresponding actions, lowering the effort of tennis instructors and significantly raising the standard of tennis instruction.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.3188