Research on the construction of cheerleading technique evaluation and teaching system integrating deep visual recognition and cognitive feedback mechanism

This paper describes an advanced system for the evaluation and teaching of cheerleading techniques that combines deep vision perception with cognitive feedback mechanisms. The method uses spatial feature extraction via convolutional neural networks and temporal movement analysis with recurrent neural networks, along with 3D pose estimation, to facilitate automatic evaluation of the techniques. A cognitive feedback mechanism, which is multi-modal in nature and draws principles from motor learning, supplies customized teaching through the visual, auditory, and haptic pathways. The system achieves 92.4% accuracy in technique classification with real-time processing at 35.4 fps, reduces training time by 35%, and improves skill retention to 89.3% at 4 weeks post-training compared to conventional coaching methods. The paper contributes to the application of artificial intelligence technology to sports education by providing a new way of objective analysis of performance and learning adaptation in the context of cheerleading training.