RF-Eye: Commodity RFID Can Know What You Write and Who You Are Wherever You Are

Handwriting recognition systems have greatly enhanced AIoT applications, especially in human-computer interaction. Wireless-based methods, favored for their non-invasive nature and ease of deployment, are becoming more common. However, existing works, which typically depend on the user’s position, often perform poorly in varied writing positions. Additionally, they do not incorporate user identity information, which could lead to security vulnerabilities by failing to reject unauthorized users. To address these issues, this article introduces RF-Eye , a system that enables contactless, position-independent handwriting recognition and user identification without prior training. Its innovative approach uses each Radio-frequency identification (RFID) tag as a unique viewpoint for observing hand movements and employs pairs of tags to track directional changes. Specifically, building upon the signal transmission model and the Fresnel Zone, we propose a novel feature, DCG , to capture changes in gesture direction and confirm its consistency across different positions. Based on DCG , we develop unique patterns for common handwriting symbols that enhance our recognition algorithm. Moreover, to strengthen the system security, we link these patterns with distinct handwriting styles through the extraction of finer-grained features, thus, preventing the misuse of the system by unauthorized users. Extensive experiments demonstrate RF-Eye ’s efficacy, which achieves recognition accuracies of 93.5%, 95.2%, and 95.8% for 26 lowercase letters, 10 digits, and 10 graphic symbols, respectively, and identifying unauthorized users with 98.6% accuracy.