Motion Recognition Based on Deep Learning and Human Joint Points

In order to solve the problem that the traditional feature extraction methods rely on manual design, the research method is changed from the traditional method to the deep learning method based on convolutional neural networks. The experimental results show that the larger average DTW occurs near the 55th calculation, that is, about the 275th frame of the video. In the 55th calculation, the joint angle with the largest DTW distance is the right knee joint. A multiscene action similarity analysis algorithm based on human joint points has been realized. In the fitness scene, by analyzing the joint angle through cosine similarity, the time of fitness key posture in the action sequence can be recognized. In the sports scene, through the similarity analysis of joint angle sequences by the DTW algorithm, we can get the similarity between people's actions in the sports video and the joint positions with large differences in some time intervals, and the real validity of the experiment is verified. The accuracy of motion recognition before and after the improvement is 95.2% and 97.1%, which is 0.19% higher than that before the improvement. The methods and results are widely used in the fields of sports recognition, movement specification, sports training, health management, and so on.