用边缘计算与强化学习实现实时田径运动员监测，响应更快更准。
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
- 在物联网架构中融合SAC优化的深度学习模型，实现低延迟运动识别。
- 实验显示响应时间、处理精度和能效均显著优于传统方法。
- 适合体育科研、智能训练系统开发者参考，尤其适用于复杂赛事场景。
本研究聚焦田径运动员的实时监测与分析，解决传统系统在实时性与准确性方面的局限。提出一种面向物联网优化的系统，集成边缘计算与深度学习算法。传统系统在处理复杂运动数据时常出现延迟和精度下降，而本方法通过在物联网架构中引入SAC优化的深度学习模型，实现了高效的运动识别与实时反馈。实验结果表明，该系统在响应时间、数据处理精度和能量效率方面显著优于传统方法，尤其在复杂田径项目中表现突出。本研究不仅提升了运动员监测的精确性与效率，也为体育科学研究提供了新的技术支持与应用前景。
原文摘要 · Abstract (English)
This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often experience delays and reduced accuracy when handling complex motion data, whereas our method, by incorporating a SAC-optimized deep learning model within the IoT architecture, achieves efficient motion recognition and real-time feedback. Experimental results show that this system significantly outperforms traditional methods in response time, data processing accuracy, and energy efficiency, particularly excelling in complex track and field events. This research not only enhances the precision and efficiency of athlete monitoring but also provides new technical support and application prospects for sports science research.
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