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.

翻译：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与运动优化。IE-PONet整合了C3D用于时空特征提取、OpenPose用于实时关键点检测，以及贝叶斯优化用于超参数调优。在NTURGB+D和FineGYM数据集上的实验结果表明其性能优越，AP\(^p50\)分数分别达到90.5和91.0，mAP分数分别为74.3和74.0。消融研究证实了各模块在提升模型精度中的关键作用。IE-PONet为运动表现分析与优化提供了稳健的工具，为训练与损伤预防提供了精确的技术洞察。未来工作将聚焦于进一步模型优化、多模态数据整合以及开发实时反馈机制，以增强实际应用价值。