Open AccessFMCW Radar Based In-Air Alphanumeric Gesture Recognition with Machine Learning
Author(s) -
Wancheol Kim,
Jun Byung Park,
Shahzad Ahmed,
Sung Ho Cho
Publication year - 2025
Publication title -
ieee transactions on instrumentation and measurement
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.82
H-Index - 119
eISSN - 1557-9662
pISSN - 0018-9456
DOI - 10.1109/tim.2025.3573779
Subject(s) - power, energy and industry applications , components, circuits, devices and systems
The rapid advancement in computing devices and their integration into daily lives is constantly increasing the importance of natural human–computer interfaces. In recent years, in-air writing gesture recognition using radars has gained substantial attention. Given that several alphabet and digit patterns are highly similar, existing studies perform alphabet and number recognition separately, often by using multiple radars. Unlike existing studies, this study develops a new framework to recognize 43 gestures, including 36 alphanumerics and 7 special characters, using a single non-contact frequency-modulated continuous-wave (FMCW) radar. Hand movement is tracked using range, Doppler, and angle information extracted using the FMCW radar to form a drawing pattern that serves as an input to a ShuffleNet-based deep learning model. Data from 14 participants are collected from three locations for performance evaluation. The system achieves a promising accuracy of 93.1%, validating its reliability and efficiency in real-world setting.