Ultra-Fast Device-Free Visible Light Sensing and Localization via Reflection-Based {\Delta}RSS and Deep Learning

TL;DR

This paper introduces a rapid, device-free visible light sensing and localization system that uses reflection-based signal variations and deep learning to accurately detect human presence and position non-intrusively.

Contribution

It presents a novel VLC-based system utilizing reflection modeling and deep neural networks for fast, accurate, and adaptable human sensing and localization.

Findings

High accuracy in human presence detection

Fast response times for real-time applications

Versatile system adaptable to various scenarios

Abstract

We propose an Ultra-Fast, Device-Free Visible Light Sensing and Positioning system that captures spatiotemporal variations in single-LED VLC channel responses, using ceiling-mounted photodetectors, to accurately and non-intrusively infer human presence and position through optical signal reflection modeling. The system is highly adaptive and ready to serve different real-world sensing and positioning scenarios using one or more ML based models from the library of multi-architecture deep neural network ensembles we have developed.