Development of Intelligent Remote Monitoring and Control System for Sports Physical Fitness based on 5G Network

In view of the high network latency and unstable data transmission of traditional sports monitoring systems, this paper proposes a solution for the development of intelligent sports monitoring and control systems based on 5G networks. A six-axis inertial sensor, photoelectric heart rate sensor and surface electromyography sensor are integrated to build a wearable terminal, and 5G slicing technology is used to achieve synchronous transmission of multimodal data. Data preprocessing is combined with edge computing nodes to build a multi-task deep learning model (CNN-BiLSTM hybrid network) to analyze sports posture, muscle load and metabolic equivalent in real time. The system reduces end-to-end latency to less than 6 milliseconds, achieves 0.5-second abnormal action warning in five types of sports such as basketball and swimming, and the correlation coefficient between dynamic load prediction and true value reaches 0.93. The system provides a high-precision, low-latency remote monitoring solution for competitive sports and rehabilitation training.