Study on Joint Injury of Taijiquan Movement Based on Computer Image Analysis

As a traditional Chinese martial art, Taijiquan has a remarkable effect on the rehabilitation of joint injury with its unique movement. In this paper, the influence of Taijiquan on joint injury is analyzed by using the local depth feature representation method of image sampling. Then, the local feature coding algorithm is introduced, and the problems existing in the rehabilitation of joint injury are analyzed. An analysis algorithm of the influence of Taijiquan on joint injury based on CV model was proposed, and the effectiveness of the algorithm was verified. The results show that the proposed algorithm improves the MS-COCO dataset by 0.2%, 0.88%, 1.86% and 3.18%, respectively, compared with Hash Net. On the 15Scene dataset, CNN-VLAD’s classification results were 4.1% higher than those of the TNNCV model. On the Caltech 256 data set, the classification accuracy of SMVLADC algorithm is 7.7% higher than CNN-VLAD algorithm. This shows that the proposed algorithm is effective, and the local depth features extracted by CNN are more effective than the traditional artificial features. At the same time, the superiority of CV model based on improved significant regional features is further verified. This study provides a new theoretical basis and practical method for the rehabilitation treatment of joint injury by Taijiquan.