Real-Time Hand Motion-Modulated Chipless RFID With Gesture Recognition Capability

• Published in: IEEE transactions on microwave theory and techniques 2024-09, p.1-14
• Main Authors: Azarfar, Ashkan, Barbot, Nicolas, Perret, Etienne
• Format: Article
• Language: English
• Subjects: Backscatter; Backscatter modulation; Chipless RFID; differential radar cross-section (RCS); Engineering Sciences; Frequency modulation; Gesture recognition; hand gesture recognition; motion-modulated chipless radio frequency identification (RFID); Radiofrequency identification; Real-time systems; Resonant frequency
• ISSN: 0018-9480
• DOI: 10.1109/TMTT.2024.3454464

This article presents a real-time identification process for slow motion-modulated chipless radio frequency identification (RFID), which can be applied to human hand gestures to realize a full-practical long-range RFID system with gesture recognition capability. Depolarizing chipless tags configured by hand in three gestures are utilized, while different motion-induced modulation effects involved in each gesture are analytically modeled. The identification based on differential radar cross-section (RCS) and gesture recognition using deterministic in-phase/quadrature (IQ) trajectories is addressed according to the developed model. The fast reading process for slow motion-modulated chipless RFID is proposed based on the sequential fast frequency sweep acquisitions by vector network analyzer (VNA), while the sweep time is very short compared to the period of motion. The identification time achieved using this approach is about only a few periods of motion, which is ideal for implementing hand gesture-modulated chipless RFID in real time. The designed gesture-modulated chipless tag is identified during a few seconds at distances up to 3 m for the most efficient gesture. Moreover, all the hand gestures are recognized at the 2-m range based on the received IQ signature. These achievements realize the practical example of motion-modulated chipless RFID where both tag identification and gesture recognition are simultaneously provided.