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

TL;DR

This paper presents an IoT-based system utilizing edge computing and deep reinforcement learning to enable real-time, accurate monitoring and analysis of track and field athletes, improving response time and data accuracy over traditional methods.

Contribution

It introduces a SAC-optimized deep learning model integrated into an IoT architecture for efficient, real-time athlete motion recognition and feedback.

Findings

System outperforms traditional methods in response time

Achieves higher data processing accuracy

Enhances energy efficiency in complex events

Abstract

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.