Digit Recognition in Air-Writing Using Single Millimeter-Wave Band Radar System

• Published in: IEEE sensors journal Vol. 22; no. 10; pp. 9387 - 9396
• Main Authors: Lee, Hyeonmin, Lee, Yonghee, Choi, Hanho, Lee, Seongwook
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air-writing; Artificial neural networks; Chirp; Continuous radiation; convolutional neural network; Data acquisition; digit recognition; Doppler radar; frequency-modulated continuous wave radar; Horizontal orientation; Hough transform; Hough transformation; Millimeter waves; Radar; Radar antennas; Radar detection; Radar equipment; Radar systems; Receiving antennas; Recognition; Sensors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3164858

In this paper, we propose an air-writing method in a millimeter-wave band radar system. In particular, a method for removing undesired detection results due to a hand movement is proposed. In our experiments, we use a frequency-modulated continuous wave (FMCW) radar system using 62 GHz as the center frequency and 3 GHz as the bandwidth, which has a range resolution of several centimeters. After installing the FMCW radar on the table, radar sensor data is acquired by having subjects write single-digit numbers (i.e., numbers 0 to 9) in the air. However, in the case of writing numbers 4 and 5, even unnecessary hand movements can be detected by the radar sensor. To identify the numbers in which such undesired detection results occur, the Hough transform is applied to the detection result in the horizontal direction. Then, using different features for each number in the Hough transform domain, undesired detection results due to the hand movement that interfere with number recognition is removed. Finally, we evaluate the digit recognition performance with a convolutional neural network-based classifier. When undesired detection results are removed by the proposed method, the numbers can be recognized with an average accuracy of 97%.