Mmtaster: A Mobile System for Fine-Grained and Robust Alcohol Sensing

Wireless sensing offers a promising approach to identify the content of liquids without opening the container or directly touching the liquid. Although existing methods aim to achieve fine-grained identification, i.e., distinguishing a 1% v/v difference in alcohol content, they still have limitations in detecting highly deceptive counterfeit liquors that have much smaller content differences, sometimes as low as 0.2% v/v alcohol content. In this paper, we propose mmTaster, a mobile system that combines the mmWave radar with a smartphone to perform fine-grained and robust alcohol sensing. To achieve the desired fine granularity, we introduce a novel feature extraction model that exploits the unique reflection responses across multiple mmWave frequencies, which provide discriminative information about liquid content. Furthermore, we observe the serious interference of target displacement on identification performance, which hinders the various applications in mobile scenarios. To enhance the robustness, mmTaster incorporates a customized translation-invariant neural network, ConvNet, to remove the location interference and extract stable liquid-dependent features regardless of target displacement. Extensive experimental results demonstrate that mmTaster can accurately distinguish the alcohol differences as low as 0.2% v/v with an accuracy of over 90.8% even in scenarios involving diverse displacements and rotations.