Real-Time Monitoring and Data Analysis System of Sports Equipment Based on Industrial Internet of Things

The rapid advancement of the Industrial Internet of Things (IIoT) has revolutionized sports health management by enabling real-time monitoring of athletes' physiological and kinematic data. However, existing systems face challenges in real-time performance, accuracy, and noise resilience during data acquisition and analysis. This study aims to develop a real-time monitoring and data analysis system for sports equipment by integrating wavelet transform and an improved K-nearest neighbors (KNN) algorithm to enhance classification accuracy and system responsiveness. The proposed system employs wavelet transform for multi-resolution noise reduction and feature extraction from sensor data (e.g., acceleration, heart rate, gyroscope). An enhanced KNN algorithm dynamically adjusts feature weights and distance metrics to optimize classification. Experiments were conducted on running, cycling, and weightlifting activities using Raspberry Pi 4 edge devices and wireless sensors (LoRa/ZigBee). Compared to traditional KNN (83% accuracy), the proposed method achieves 89% accuracy, an improvement of 6 percentage points, and reduces system response time from 200 ms to 50 ms (a 75% improvement). This work demonstrates a robust IIoT-based framework for intelligent sports analytics, offering high-precision, low-latency monitoring applicable to training optimization, injury prevention, and personalized health management.