When learning meets RFIDs: The case of activity identification

Over the past decades have seen booming interests in human activity identification that is widely used in a range of Internet-of-Things applications, such as healthcare and smart homes. It has attracted significant attention from both academia and industry, with a wide range of solutions based on cameras, radars, and/or various inertial sensors. They generally require the object of identification to carry sensors/wireless transceivers, which are not negligible in both size and weight, not to mention the constraints from the battery. Radio frequency identification (RFID) is a promising technology that can overcome those difficulties due to its low cost, small form size, and batterylessness, making it widely used in a range of mobile applications. 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 RFID-based activity identification solutions are far from being satisfactory. 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 thesis, we first reviewed the literature and research challenges of multipath effects in activity identification with RFIDs. Then we introduced three advanced RFID learning-based activity identification frameworks, i.e., i2tag, TagFree and M2AI, for tag mobility profiling, RFID-based device-free activity identification and tag-attached multi-object activity identification, respectively. Our extensive experiments further demonstrate their superiority on activity identification in the multipath-rich environments.