TagFree Activity Identification with RFIDs

Human activity identification plays a critical role in many Internet-of-Things applications, which is typically achieved through attaching tracking devices, e.g., RFID tags, to human bodies. The attachment can be inconvenient and considered intrusive. A tag-free solution instead deploys stationary tags as references, and analyzes the backscattered signals that could be affected by human activities in close proximity. The information offered by today's RFID tags however are quite limited, and the typical raw data (RSSI and phase angles) are not necessarily good indicators of human activities (being either insensitive or unreliable as revealed by our realworld experiments). As such, existing tag-based activity identification solutions are far from being satisfactory, not to mention tag-free. It is also well known that the accuracy of the readings can be noticeably affected by multipath, which unfortunately is inevitable in an indoor environment and is complicated with multiple reference tags.

In this paper, we however argue that multipath indeed brings rich information that can be explored to identify fine-grained human activities. Our experiments suggest that both the backscattered signal power and angle are correlated with human activities, impacting multiple paths with different levels. We present TagFree, the first RFID-based device-free activity identification system by analyzing the multipath signals. Different from conventional solutions that directly rely on the unreliable raw data, TagFree gathers massive angle information as spectrum frames from multiple tags, and preprocesses them to extract key features. It then analyzes their patterns through a deep learning framework. Our TagFree is readily deployable using off-the-shelf RFID devices and a prototype has been implemented using a commercial Impinj reader. Our extensive experiments demonstrate the superiority of our TagFree on activity identification in multipath-rich environments.