Demo Abstract: Environment-adaptive 3D Human Pose Tracking with RFID

RF-based human pose estimation has attracted increasing interest in recent years. Compared with vision-based approaches, RF-based techniques can better protect user’s privacy and are robust to lighting and non-line-of-sight conditions. However, due to complicated indoor propagation environments, most of the RF-based sensing approaches are sensitive to the deployment environment and hard to adapt to new environments. In this demo, we present a meta-learning-based approach to address the environment adaptation problem and design an environment-adaptive Radio-Frequency Identification (RFID) based 3D human pose tracking system. The system utilizes commodity RFID tags to estimate 3D human pose and leverage meta-learning algorithms to improve the environment adaptability. Experiments conducted in various environments demonstrate the high pose estimation performance and adaptability to environments.