基于预测模型的武术翻腾个性化学习路径选择研究

Introduction Meeting relevant prerequisites may improve movement readiness and skill development. In Martial Arts Tricking (MAT), diverse movement categories complicate individualized learning pathway selection. This proof-of-concept study investigates whether supervised machine learning can support personalized training by predicting trick performance and identifying influential athlete-specific features. Methods Synthetic data representing plausible feature relationships were used to demonstrate the framework, not to infer real-world athlete performance. Multiple models were evaluated for pr