mmLock: User Leaving Detection Against Data Theft via High-Quality mmWave Radar Imaging.

Jiawei Xu, Ziqian Bi, Amit Singha,Tao Li,Yimin Chen,Yanchao Zhang


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The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user's 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios.
3D human mesh data,authentication techniques,data security,data theft,explainable user point clouds,gesture recognition systems,high-quality mmWave radar imaging,hybrid PointNet-LSTM model,mmWave FMCW radar,multiple moving objects,user leaving detection,user privacy
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