Vibe2Spike: Batteryless Wireless Tags for Vibration Sensing with Event Cameras and Spiking Networks

TL;DR

Vibe2Spike introduces a batteryless wireless vibration sensing system using optical communication and spiking neural networks, enabling scalable, energy-efficient activity recognition for smart environments.

Contribution

It presents a novel battery-free sensing framework combining vibration energy harvesting, visible light communication, and optimized spiking neural networks for activity recognition.

Findings

Achieved 94.9% classification accuracy across five device classes.

Demonstrated energy efficiency and scalability of the system.

Analyzed latency-accuracy trade-offs with different temporal binning strategies.

Abstract

The deployment of dense, low-cost sensors is critical for realizing ubiquitous smart environments. However, existing sensing solutions struggle with the energy, scalability, and reliability trade-offs imposed by battery maintenance, wireless transmission overhead, and data processing complexity. In this work, we present Vibe2Spike, a novel battery-free, wireless sensing framework that enables vibration-based activity recognition using visible light communication (VLC) and spiking neural networks (SNNs). Our system uses ultra-low-cost tags composed only of a piezoelectric disc, a Zener diode, and an LED, which harvest vibration energy and emit sparse visible light spikes without requiring batteries or RF radios. These optical spikes are captured by event cameras and classified using optimized SNN models evolved via the EONS framework. We evaluate Vibe2Spike across five device classes, achieving 94.9\% average classification fitness while analyzing the latency-accuracy trade-offs of different temporal binning strategies. Vibe2Spike demonstrates a scalable, and energy-efficient approach for enabling intelligent environments in a batteryless manner.