Basketball Motion Posture Recognition Based on Recurrent Deep Learning Model

In order to improve the training effect of athletes and effectively identify the movement posture of basketball players, we propose a basketball motion posture recognition method based on recurrent deep learning. A one-dimensional convolution layer is added to the neural network structure of the deep recurrent Q network (DRQN) to extract the athlete pose feature data before the long short-term memory (LSTM) layer. The acceleration and angular velocity data of athletes are collected by inertial sensors, and the multi-dimensional motion posture features are extracted from the time domain and frequency domain, respectively, and the posture recognition of basketball is realized by DRQN. Finally, the new reinforcement learning algorithm is trained and tested in a time-series-related environment. The experimental results show that the method can effectively recognize the basketball motion posture, and the average accuracy of posture recognition reaches 99.3%.