用物联网技术提升运动员三维姿态估计与动作优化精度
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose
- 融合C3D、OpenPose与贝叶斯优化，实现高精度姿态捕捉
- 在两个数据集上达到90.5和91.0的AP@50，mAP达74.0以上
- 适合体育训练分析与运动损伤预防，支持实时反馈
本研究提出基于物联网的姿势优化网络（IE-PONet），用于田径运动员的高精度三维姿态估计与动作优化。IE-PONet结合C3D进行时空特征提取，OpenPose实现实时关键点检测，并采用贝叶斯优化进行超参数调优。在NTURGB+D和FineGYM数据集上的实验结果表明，其AP@50分别达到90.5和91.0，mAP分别为74.3和74.0。消融实验验证了各模块对模型精度提升的关键作用。IE-PONet为运动表现分析与优化提供了可靠工具，可提供精准的技术洞察，助力训练与伤防。未来工作将聚焦模型进一步优化、多模态数据融合及实时反馈机制开发。
原文摘要 · Abstract (English)
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.
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