Enhancing Player Experience Through AI-Powered Wireless Sensor Networks: A KNN Algorithm Approach for Tracking Daily and Sports Activities

This research introduces an innovative implementation of AI-based wireless sensor networks (WSNs) designed to revolutionize player experiences across daily activities and sports engagements. While existing systems have shown promise, they often must capture the intricacies of players’ movements and actions, leading to incomplete insights and suboptimal performance optimization. This study presents a novel approach leveraging the K-nearest neighbours (KNN) algorithm, which excels at pattern recognition tasks. By combining the strengths of AI and WSNs, our system enables real-time tracking, precise monitoring, and comprehensive data-driven analyses of players’ actions. We meticulously deploy sensor nodes, gather data, pre-process information, extract relevant features, and employ the KNN algorithm for activity recognition. Rigorous experimentation validates the system’s capability to capture even the most complex activity patterns. This research advances the field of WSNs in sports analytics and furnishes vital insights for fine-tuning training regimens and elevating overall player performance.