@inproceedings{7544cd5e54744b6487acc73c504e4d41,
title = "Through-Screen Visible Light Sensing Empowered by Embedded Deep Learning",
abstract = "Motivated by the trend of realizing full screens on devices such as smartphones, in this work we propose through-screen sensing with visible light for the application of fingertip air-writing. The system can recognize handwritten digits with under-screen photodiodes as the receiver. The key idea is to recognize the weak light reflected by the finger when the finger writes the digits on top of a screen. The proposed air-writing system has immunity to scene changes because it has a fixed screen light source. However, the screen is a double-edged sword as both a signal source and a noise source. We propose a data preprocessing method to reduce the interference of the screen as a noise source. We design an embedded deep learning model, a customized model ConvRNN, to model the spatial and temporal patterns in the dynamic and weak reflected signal for air-writing digits recognition. The evaluation results show that our through-screen fingertip air-writing system with visible light can achieve accuracy up to 91%. Results further show that the size of the customized ConvRNN model can be reduced by 94% with less than a 10% drop in performance.",
keywords = "embedded AI, embedded deep learning, Through-screen sensing",
author = "Hao Liu and Hanting Ye and Jie Yang and Qing Wang",
year = "2021",
doi = "10.1145/3485730.3493454",
language = "English",
series = "SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery (ACM)",
pages = "478--484",
booktitle = "SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems",
address = "United States",
note = "19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021 ; Conference date: 15-11-2021 Through 17-11-2021",
}