AI-Enhanced Triboelectric Surface Analysis for Smart Urban Accessibility Pattern Detection

By combining triboelectric sensors with artificial intelligence, this study offers a novel method for detecting patterns in urban accessibility. We present TriboPATH (Triboelectric Pattern Analysis for Transit Heatmaps), a novel framework that integrates deep learning and triboelectric signal processing for real-time accessibility pattern recognition, building on previous work in self-powered angle sensors and urban analytics. Through the use of smart surfaces equipped with triboelectric sensors, the system records movement patterns and uses a specially designed neural network architecture to convert them into accessibility measures. Our solution outperforms current approaches by achieving 94.7% accuracy in pattern recognition over a range of view distances and angles. The system provides a long-term solution for smart city accessibility monitoring, exhibiting strong performance across 13 global metropolitan datasets.