Technical Action Recognition Algorithm for Cheerleaders Based on Inertial Sensors and Pose Motion Capture

Aiming at the problems of low efficiency and strong subjectivity in cheer movement recognition, this paper proposes a recognition algorithm based on inertial sensor and improved spatiotemporal hypergraph convolution network. Firstly, high-precision 3D bone data is obtained by integrating inertial sensor and attitude capture technology. Secondly, the self-attention mechanism is embedded in the hypergraph convolutional network to dynamically assign joint weights, and the time-sparse hypergraph and channel sparse hypergraph are designed to achieve joint optimization of spatiotemporal features. The experimental results show that the accuracy rate, recall rate and F1 value of the proposed algorithm in the benchmark test are 0.96, 0.98 and 0.97 respectively, which is significantly better than the traditional algorithm. In the practical application test, the recognition accuracy rate of swing movements is as high as 99.2%, and the average recognition time of four types of movements is less than 0.1 seconds. This study combines hypergraph convolution with spatiotemporal sparse optimization for the first time, providing a new method for complex multi-joint movement recognition, and an efficient and objective technical tool for cheerleading training and competition evaluation.