Recognizing hand gestures using solar cells

• Main Authors: MA, Dong, LAN, Guohao, HASSAN, Mahbub, HU, Wen, UPAMA, B. Mushfika, UDDIN, Ashraf, YOUSEEF, Moustafa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Photovoltaic Cells; Photoconductivity; Gesture Recognition; Solar Panels; Energy Harvesting; Three Dimensional Displays; Standards; Solar Energy Harvesting; Visible Light Sensing; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7014; https://ink.library.smu.edu.sg/context/sis_research/article/8017/viewcontent/Recognizing_Hand_Gestures_using_Solar_Cells.pdf
• Institution: Singapore Management University

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 a22solar cell array. The power measurement study suggests that SolarGest consume 44% less power compared to light sensor based systems.