Research on the Integrated Method of Classification and Counting of Fitness Activities

Mass fitness activities are becoming increasingly popular, and it is of great significance to automatically identify fitness exercise categories and counting. Fitness+ artificial intelligence is the future development trend. This paper proposes an integrated method to automatically identify the type of exercise and count the frequency of exercise. On the basis of extracting human joint points, the spatiotemporal graph convolutional network is improved by adding spatial attention modules and temporal dilated convolutional module to identify different types of motion. After identifying the type of motion, the frequency of movement is judged by the changes of the angle characteristics of the human joint point, realizing the integration of fitness activities classification and counting. Finally, the paper conducted experiments on related dataset, where the classification accuracy rate reaches 91.2%, indicating that the network model designed achieved good recognition effects, and the counting accuracy rate reaches 93.4%, indicating the feasibility and effectiveness of the proposed counting method.