Autonomous Sensor Node System for Table Tennis Officiating: Optimized Placement Using Enhanced Chimp Optimization Algorithm

Accurate table tennis officiating for side ball decisions and net is challenging due to human error and the high cost of systems like Hawk-Eye, which require numerous sensors and lack adaptability. This study proposes an Autonomous Sensor Node System (ASNS) with three triboelectric nanogenerator (TENG)-powered acceleration sensors, optimized by the Enhanced Chimp Optimization Algorithm (ECOA) for collision point (CP) detection.

乒乓球赛事中针对擦边球与球网相关判罚的精准执法颇具挑战：一方面存在人为失误风险，另一方面诸如鹰眼（Hawk-Eye）这类需部署大量传感器且适配性欠佳的判罚系统成本高昂。本研究提出一种搭载三台由摩擦纳米发电机（Triboelectric Nanogenerator，TENG）供电的加速度传感器的自主传感器节点系统（Autonomous Sensor Node System，ASNS），并通过增强型黑猩猩优化算法（Enhanced Chimp Optimization Algorithm，ECOA）对碰撞点（Collision Point，CP）检测任务进行优化。