ENHANCING THE PERFORMANCE STANDARDS OF SPORTS EQUIPMENT THROUGH ONLINE MONITORING AND BIG DATA ANALYSIS

Abstract

Online monitoring, utilizing big data analytics, is critical in ensuring the performance standards of sports equipment meet rigorous accuracy and reliability criteria. This study introduces the Improved Runge Kutta Optimized Non-Sequential Autoregressive Exogenous Model (IRKO-NAEM) for real-time monitoring and enhancement of sports equipment performance. This advanced model aims to ensure that equipment used in sports settings, such as wearable devices and training machinery, operates within specified performance standards, providing accurate data crucial for athlete training and performance analysis. The methodology involves utilizing a public database simulating various sports equipment usage scenarios. Data preprocessing includes Min-Max normalization to enhance accuracy and consistency across measurements. Feature extraction uses Total Harmonic Distortion (THD) to detect anomalies in equipment operation early, potentially identifying issues before they affect performance or safety. The effectiveness of the IRKO-NAEM method is evaluated using precision, recall, F1-score, and mean squared error (MSE) metrics, providing a comprehensive assessment of its capability to improve equipment accuracy and reliability. Experimental results demonstrate that the IRKO-NAEM method significantly enhances the standard performance of sports equipment, contributing to the safety, efficiency, and reliability of training and competitive environments in sports.