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 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.