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.

With the rapid advance of smart home and office systems, it becomes possible to provide a fine-grained user activity tracking service accurately recognizing user activities and identities in a seamless and non-invasive manner. Such a system can find applications in various domains, such as elder safeguard, customized services, and simply personal activity diary. Recently, several radio frequency (RF) based sensing systems were proposed for human sensing, most of which focus on limited scenarios and suffer from interference caused by other users or wireless devices. To tackle this challenge, we propose Back-Guard, which achieves accurate and non-intrusive user activity recognition and then user identification through battery-free wireless backscattering. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains that can characterize the user behaviors. Leveraging the parallel attention based deep learning models, our system can discriminate different motions and users accurately and robustly in various situations. We implement a prototype system and collect data in actual scenarios from 25 users for over 2 months. Extensive experiments demonstrate the promising performance of our system. In particular, Back-Guard achieves 93.4% activity recognition accuracy and 91.5% user identification accuracy, respectively. Our experiments also demonstrate little accuracy reduction when multiple users are separated, e.g., by around 2 meters.