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Free, publicly-accessible full text available May 19, 2026
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Nguyen, Khang; Ren, Yidong; Du, Jialuo; Lin, Jingkai; Gan, Maolin; Chen, Shigang; Zhang, Mi; Peng, Chunyi; Cao, Zhichao (, ACM)Free, publicly-accessible full text available June 11, 2026
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Zhang, Shichen; Wang, Qijun; Gan, Maolin; Cao, Zhichao; Zeng, Huacheng (, Network and Distributed System Security (NDSS) Symposium)This paper aims to design and implement a radio device capable of detecting a person’s handwriting through a wall. Although there is extensive research on radio frequency (RF) based human activity recognition, this task is particularly challenging due to the through-wall requirement and the tiny-scale handwriting movements. To address these challenges, we present RadSee—a 6 GHz frequency modulated continuous wave (FMCW) radar system designed for detecting handwriting content behind a wall. RadSee is realized through a joint hardware and software design. On the hardware side, RadSee features a 6 GHz FMCW radar device equipped with two custom-designed, high-gain patch antennas. These two antennas provide a sufficient link power budget, allowing RadSee to “see” through most walls with a small transmission power. On the software side, RadSee extracts effective phase features corresponding to the writer’s hand movements and employs a bidirectional LSTM (BiLSTM) model with an attention mechanism to classify handwriting letters. As a result, RadSee can detect millimeter-level handwriting movements and recognize most letters based on their unique phase patterns. Additionally, it is resilient to interference from other moving objects and in-band radio devices. We have built a prototype of RadSee and evaluated its performance in various scenarios. Extensive experimental results demonstrate that RadSee achieves 75% letter recognition accuracy when victims write 62 random letters, and 87% word recognition accuracy when they write articles.more » « less
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Liu, Yimeng; Gan, Maolin; Zeng, Huaili; Liu, Li; Dong, Younsuk; Cao, Zhichao (, ACM)
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