Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm

• DOI: 10.48550/arxiv.2411.06720
• Publication Date: 2024-11-11
• Authors: Xiaowei Tang; Bin Long; Li Zhou
• Keywords: Deep reinforcement learning; Signal Processing (eess.SP); FOS: Computer and information sciences; Computer Science - Machine Learning; Track and field athletes; FOS: Electrical engineering, electronic engineering, information engineering; IoT optimization; Edge computing; TA1-2040; Electrical Engineering and Systems Science - Signal Processing; Engineering (General). Civil engineering (General); Real-time athlete monitoring; Machine Learning (cs.LG)

This research focuses on real-time monitoring and analysis of track field athletes, addressing the limitations traditional systems in terms performance accuracy. We propose an IoT-optimized system that integrates edge computing deep learning algorithms. Traditional often experience delays reduced accuracy when handling complex motion data, whereas our method, by incorporating a SAC-optimized model within IoT architecture, achieves efficient recognition feedback. Experimental results show this significantly outperforms methods response time, data processing accuracy, energy efficiency, particularly excelling events. not only enhances precision efficiency athlete but also provides new technical support application prospects for sports science research.