# SolarGest: Ubiquitous and Battery-free Gesture Recognition using Solar Cells

arXiv: https://arxiv.org/abs/1812.01766 · HTML: https://mlsanity.com/paper/1812.01766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [LightLLM: A Versatile Large Language Model for Predictive Light Sensing](https://mlsanity.com/paper/2411.15211/) (2024)
- [TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation](https://mlsanity.com/paper/2411.16020/) (2024)
- [Cricket: A Self-Powered Chirping Pixel](https://mlsanity.com/paper/2505.02246/) (2025)
- [Mask2Hand: Learning to Predict the 3D Hand Pose and Shape from Shadow](https://mlsanity.com/paper/2205.15553/) (2022)

## Abstract (arXiv)

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.