RFnet: Automatic Gesture Recognition and Human Identification Using Time Series RFID Signals

Utilizing wireless signals for gesture recognition and human identification is an emerging type of technology for touchless user interface, which allows the computer to automatically identify the user and interpret his/her gestures as commands. Such techniques extract features to profile the fluctuation of time series wireless signals to infer human gestures/identities. Among which, device-free approach becomes more attractive because it does not need human to carry or wear sensing devices. Existing device-free solutions, though yielding good performance, require heavy crafting on data preprocessing and feature extraction. In this paper, we propose RFnet, a multi-branch 1D-CNN based framework, that explores the possibility of directly utilizing time series RFID signal to recognize static/dynamic gestures as well as the identity of users, which can benefit a large number of applications such as smart homes where security is also a prior concern. We conduct extensive experiments in three different environments. The results demonstrate the superior effectiveness of the proposed RFnet framework.