WiRITE: General and Practical Wi-Fi Based Hand-Writing Recognition

• Published in: IEEE transactions on mobile computing Vol. 23; no. 4; pp. 1 - 15
• Main Authors: Zhang, Yanbo, Sun, Weiping, Li, Mo
• Format: Magazine Article
• Language: English
• Published: Los Alamitos IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Antenna measurement; CSI sanitization; gesture recognition; Hand (anatomy); human computer interaction; Human motion; Human-computer interface; Image classification; Machine learning; Mobile computing; Recognition; Training; Trajectory; wi-fi sensing; Wireless fidelity; Writing
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2023.3265988

Device-free hand-writing systems identify the content that a user writes by hand movement in the air, thus providing an intuitive human computer interface. In this paper, we propose WiRITE, a Wi-Fi hand-writing recognition system built with commodity Wi-Fi APs. Unlike most existing machine learning based hand-writing recognition systems, which are often subject to severe limitations in generality, e.g., high training overhead when adapted across hand-writing alphabets, environments, and users, WiRITE is designed with unique consideration of its generality when applied to practice-being application-transferable, environment-agnostic, and user-independent. With little training overhead, WiRITE behaves inclusively to different users, environments, and applications, stemming from a comprehensive design of signal processing that is built into its core machine learning model. Extensive evaluation is conducted with five users for three applications, i.e., recognizing Digits, English letters, and Chinese characters, in realistic office environment. The experiment results demonstrate that WiRITE provides at least 0.9 accuracy in various combinations of users and applications with 0.93 accuracy in average.