Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
This work addresses real-time performance and accuracy limitations in athlete monitoring for sports science, though it appears incremental as it builds on existing IoT and deep learning approaches.
This research tackled real-time monitoring and analysis of track and field athletes by proposing an IoT-optimized system integrating edge computing and deep reinforcement learning, achieving significant improvements in response time, data processing accuracy, and energy efficiency compared to traditional methods.
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.