This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet integrates C3D for spatiotemporal feature extraction, OpenPose for real-time keypoint detection, and Bayesian optimization for hyperparameter tuning. Experimental results on NTURGB+D and FineGYM datasets demonstrate superior performance, with AP\(^p50\) scores of 90.5 and 91.0, and mAP scores of 74.3 and 74.0, respectively. Ablation studies confirm the essential roles of each module in enhancing model accuracy. IE-PONet provides a robust tool for athletic performance analysis and optimization, offering precise technical insights for training and injury prevention. Future work will focus on further model optimization, multimodal data integration, and developing real-time feedback mechanisms to enhance practical applications.

本研究提出了物联网增强姿态优化网络（IE-PONet），用于田径运动员的高精度三维姿态估计和运动优化。该方法通过整合C3D实现时空特征提取，利用OpenPose进行实时关键点检测，并通过贝叶斯优化进行超参数调整。实验结果表明，IE-PONet在运动表现分析和优化中具有出色性能，为训练和伤害预防提供了精确的技术洞见。

基于物联网的运动员三维姿态估计与运动优化：C3D和OpenPose的应用