Battery-free AI of Things

Despite the potential of vision-based monitoring, data leakage concerns hinder its wide deployment in personal spaces. Besides, continuous and pervasive monitoring without blind spots in complicated indoor spaces requires a scalable system. Our system presents a vision-based end-to-end action recognition framework that (i) intrinsically achieves data anonymity from the sensing stage and (ii) battery-free operation for blind spot-free continuous monitoring. Our custom tag provides > 2 fps videos even under dark places (e.g., deep in the shelf without direct light), and our system of three tags shows an accuracy of 98.16% on classifying ten actions. It leverages an extremely low-resolution (eLR) near-infrared (NIR) image sensor with 6×10 pixels for video anonymity and an RFID-compliant fully-passive tag for real-time eLR video streaming.

• [ACM/IEEE IPSN'23] Mosaic: Extremely Low-resolution RFID Vision for Visually-anonymized Action Recognition (Outstanding Paper Award by Institute of Embedded Engineering of Korea)