Back-Guard: Wireless Backscattering Based User Sensing With Parallel Attention Model

With the rapid advance of wireless sensing techniques, it becomes possible to provide a fine-grained user activity tracking service at home and office. Such a technique is of broad applications in various domains such as personal activity diary, elderly care, and customized services. For example, several radio frequency (RF) based sensing systems were recently proposed for human activity recognition. However, most of them focused on specific scenarios and suffered from interference caused by other users and wireless devices. In this work, we propose Back-Guard, a backscattering-based sensing system that achieves accurate and non-intrusive user activity recognition and further user identification/authentication. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains. Leveraging the parallel attention based deep learning model, our system can discriminate different motions and users accurately and robustly in various situations. We implemented a prototype system and collected data from 25 users for more than 2 months. Extensive experiments demonstrate that Back-Guard achieves 93.4% activity recognition accuracy and 91.5% user identification accuracy, respectively. In particular, Back-Guard can also tackle multiple user scenarios, which has little accuracy reduction when the users are separated, e.g., by around 2 meters.