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

arXiv: https://arxiv.org/abs/2508.11640 · HTML: https://mlsanity.com/paper/2508.11640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Lightweight Event-based Optical Flow Estimation via Iterative Deblurring](https://mlsanity.com/paper/2211.13726/) (2022)
- [Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning](https://mlsanity.com/paper/2404.01815/) (2024)
- [LoRa Backscatter: Enabling The Vision of Ubiquitous Connectivity](https://mlsanity.com/paper/1705.05953/) (2017)
- [Pible: Battery-Free Mote for Perpetual Indoor BLE Applications](https://mlsanity.com/paper/1812.04717/) (2018)
- [Every Byte Matters: Traffic Analysis of Bluetooth Wearable Devices](https://mlsanity.com/paper/2105.11172/) (2021)
- [Adaptively Pruned Spiking Neural Networks for Energy-Efficient Intracortical Neural Decoding](https://mlsanity.com/paper/2504.11568/) (2025)

## Abstract (arXiv)

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.