Summary of Real-time Monitoring and Analysis Of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm, by Xiaowei Tang et al.
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
by Xiaowei Tang, Bin Long, Li Zhou
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed IoT-optimized system integrates edge computing and deep learning algorithms to enable real-time monitoring and analysis of track and field athletes, overcoming traditional systems’ limitations in terms of performance and accuracy. The system utilizes a SAC-optimized deep learning model within an IoT architecture, achieving efficient motion recognition and real-time feedback. Experimental results demonstrate the system’s superiority over traditional methods in response time, data processing accuracy, and energy efficiency, particularly excelling in complex track and field events. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a real-time monitoring system for track and field athletes that is more accurate and efficient than current systems. It uses special computer chips and deep learning to quickly recognize athlete movements and provide immediate feedback. This helps coaches and athletes get better results and opens up new possibilities for sports science research. |
Keywords
* Artificial intelligence * Deep learning
