Sports Competition Assistant System Based on Fuzzy Big Data and Health Exercise Recognition Algorithm

When material desires are satisfied, people begin to pursue more and more spiritual levels. Health exercises have an excellent auxiliary effect on people’s flexibility and physical fitness, so more and more people choose health exercises. However, the movement of health exercises returns to Chengdu and affects the efficiency of physical training. Therefore, we have designed a sports competition assistance system based on vague big data and a health exercise recognition algorithm. First of all, in this article, the standard score comparison database is created by extending the standard action data. In addition, the system architecture is further given, and the key 3D data-based acquisition module design is given. In addition, the system architecture is further given, and the basic 3D data acquisition unit design is given. In this document, the depth characteristics filtered by the Fourier Pyramid are fused to the bone characteristics, and the merged data is sorted based on the support engine, thus designing the action recognition unit. A hidden Markov model (HMM) human action recognition algorithm based on pose selection is proposed. This method uses two affine propagation (AP) clustering algorithms to cluster the features, automatically select the key posture of each action, and correspond to the hidden state of the HMM. These hidden state labels are used to initialize the parameters of the HMM to train the model, and the trained model is used to implement action classification. The result shows that the design in the article has a more accurate recognition result, which provides a powerful tool for the referee to score. Using the Fourier Pyramid filtering method, through a large number of health exercises for comparison, the ability to judge the degree of standard health exercises is significantly improved, the efficiency is increased by 25%, and the accuracy rate is increased by 15%.