Shichen Zhang (Michigan State University), Qijun Wang (Michigan State University), Maolin Gan (Michigan State University), Zhichao Cao (Michigan State University), Huacheng Zeng (Michigan State University)

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 textit{through-wall} requirement and the textit{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.

View More Papers

Kronos: A Secure and Generic Sharding Blockchain Consensus with...

Yizhong Liu (Beihang University), Andi Liu (Beihang University), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhuocheng Pan (Beihang University), Yinuo Li (Xi’an Jiaotong University), Jianwei Liu (Beihang University), Song Bian (Beihang University), Mauro Conti (University of Padua)

Read More

Towards Anonymous Chatbots with (Un)Trustworthy Browser Proxies

Dzung Pham, Jade Sheffey, Chau Minh Pham, and Amir Houmansadr (University of Massachusetts Amherst)

Read More

Work-in-Progress: Detecting Browser-in-the-Browser Attacks from Their Behaviors and DOM...

Ryusei Ishikawa, Soramichi Akiyama, and Tetsutaro Uehara (Ritsumeikan University)

Read More