Recognizing Hand Gestures using Solar Cells

Journal article (2023)

Authors

Dong Ma Singapore Management University
G. Lan Embedded Systems - EEMCS
Changshuo Hu University of New South Wales
Mahbub Hassan University of New South Wales
Wen Hu University of New South Wales
Upama Mushfika University of New South Wales
Ashraf Uddin University of New South Wales
Moustafa Youssef American University in Cairo, University of Alexandria
Research Group
Embedded Systems (EEMCS) (TU Delft)
Copyright: © 2023 Dong Ma, G. Lan, Changshuo Hu, Mahbub Hassan, Wen Hu, Upama Mushfika, Ashraf Uddin, Moustafa Youssef
DOI: https://doi.org/10.1109/TMC.2022.3148143
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Embedded Systems

Abstract

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 discernible 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 demonstrate that SolarGest achieves 99% for six gestures with a single cell and 95% for fifteen gesture with a 2 × 2 solar cell array. The power measuement study suggests that SolarGest consume 44% less power compared to light sensor based systems.

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- Embargo expired in 05-12-2023