Design and Implementation of an IoT-Based Exercise Physiological Data Monitoring and Intelligent Evaluation System

To enable users to achieve a personalized and intelligent exercise experience in real-time, this paper designs and implements an exercise physiological data monitoring and intelligent evaluation system based on Internet of Things (IoT) technology. The main functions of the system are to provide users with real-time monitoring of exercise physiological parameters, evaluate exercise load, and implement intelligent warning functions based on the analysis of these parameters. The system adopts an IoT architecture and is designed with multiple layers: the perception layer, network layer, and application layer. The perception layer uses various types of sensor devices to collect key physiological parameters such as heart rate and blood oxygen saturation in real time. The network layer utilizes the ZigBee protocol to achieve local data transmission, while combining with the TCP/IP protocol for remote data communication. The application layer is mainly responsible for data processing; it analyzes the processed data to provide visual displays, generate personalized exercise prescriptions, and promptly provide intelligent warnings for exercise risks based on the analyzed database. Experimental results show that the system can effectively collect exercise physiological parameters with high accuracy, providing technical support for personalized health management and scientific training. This system offers technical references for intelligent applications in areas such as fitness, professional training, and smart elderly care.