DEVELOPMENT AND OPTIMIZATION OF AN IOT-BASED SYSTEM FOR MONITORING AND DIAGNOSTICS IN PHYSICAL HEALTH AND SPORTS PERFORMANCE

Abstract

Objective: To address the limitations in traditional monitoring systems such as data collection precision, transfer stability, and processing efficiency, this study designs an IoT-based monitoring and diagnostic system potentially applicable to health and sports performance contexts. Method: The proposed system integrates data collection terminals, a transfer network, and a cloud server. Using IoT technology and sensors, the system collects operational state and environmental information. The data is then processed and analyzed, with results transferred to and stored on a cloud server for further computation. This architecture is envisioned to monitor the physical health status and performance metrics of individuals in sports settings. Results: Experimental tests on the proposed system demonstrated an efficiency of up to 89.6% in monitoring and diagnostic tasks. This high efficiency indicates robust data handling and real-time processing capabilities, which are essential for applications in dynamic environments like sports and physical activities. Conclusion: The designed system offers stable data transmission and computational processing, significantly enhancing monitoring efficiency and diagnostic accuracy. Its application could be extended to sports medicine and athletic training, providing real-time, accurate assessments of athlete performance and health, thereby supporting optimized training regimens and injury prevention strategies.