A Machine Learning Enabled mmWave RFID for Rotational Sensing in Human Gesture Recognition and Motion Capture Applications

In the coming years, augmented reality (AR) and virtual reality (VR) based applications will become common place. The proliferation of radar technology and the strong performance of millimeter wave backscatter have presented a unique opportunity to develop low-cost and low-power solutions to support the advent of AR/VR. In this effort, the authors present a first of its kind millimeter wave backscatter RFID for rotational sensing. The novel RFID tag design employed takes advantage of the polarization mismatch of linearly polarized antennas as the angle between the pair is varied. A supervised learning algorithm is used to achieve extremely high accuracy <1° over an unambiguous range of ±90° thus opening the door for potential use in a wide variety of real-time applications.