DNN-Based Legibility Improvement for Air-Writing in Millimeter-Waveband Radar System
Seungheon Kwak, Chanul Park, Seongwook Lee · IEEE Transactions on Instrumentation and Measurement · 2023
In radar-based air-writing, the continuous measurements of the hand movements may result in the addition of unnecessary strokes for certain characters or digits (e.g., 4 and 5), making it difficult to accurately recognize the air-written results when observed by human eyes. Therefore, we propose a deep neural network (DNN)-based classifier designed to identify unnecessary strokes and clutter that arise during the radar-based air-writing. First, while air-writing the digits from 0 to 9, the range, angle, and signal amplitude of the hand movement are obtained through a radar system. Then, we represent the hand’s trajectory in the form ofxandycoordinates by using the information of range and angle. Next, we train the DNN-based classifier using the acquiredxandycoordinates, signal amplitude, and frame index as input features. To ensure the classifier’s performance would not be impacted by changes in the position and size of the air-writing area, we apply the normalization toxandycoordinates. Finally, the performance of the classifier is verified using the results of air-writing digits from 0 to 9. The proposed method identifies unnecessary strokes and clutter regardless of the position and size of the air-writing area, demonstrating an average classification accuracy of 94.57%. Furthermore, when the classifier was validated with different individuals conducting the air-writing, the classifier exhibited an average classification accuracy of 93.9%.