Long Short-Term Memory Network (LSTM) is used to Model Action Sequences

Human action recognition in sports training faces the problems of multi-source data synchronization and noise interference. This article uses timestamp synchronization technology to ensure the time consistency of video and sensor data. The processed video frames were trained and learned using a convolutional neural network, and the action sequence was modeled using a long short-term memory network, thereby improving the accuracy and robustness of recognition. The research results show that the model recognition delay is within 300ms and the resource occupancy range is between 38% and 55%, achieving efficient action recognition.