Accuracy Enhancement of Hand Gesture Recognition Using CNN

被引：6
|
作者：
Park, Gyutae [1 ]
Chandrasegar, Vasantha Kumar [1 ]
Koh, Jinhwan [1 ]
机构：
[1] Gyeongsang Natl Univ, Dept Elect Engn, Jinju 52828, Gyeongsangnam, South Korea
来源：
IEEE ACCESS | 2023年 / 11卷
基金：
新加坡国家研究基金会;
关键词：
Radar; Gesture recognition; Assistive technologies; Data models; Convolutional neural networks; Radar measurements; Hand gesture; CNN; deep learning; IR-UWB radar; 2D-Fast Fourier Transform;
D O I：
10.1109/ACCESS.2023.3254537
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Human gestures are immensely significant in human-machine interactions. Complex hand gesture input and noise caused by the external environment must be addressed in order to improve the accuracy of hand gesture recognition algorithms. To overcome this challenge, we employ a combination of 2D-FFT and convolutional neural networks (CNN) in this research. The accuracy of human-machine interactions is improved by using Ultra Wide Bandwidth (UWB) radar to acquire image data, then transforming it with 2D-FFT and bringing it into CNN for classification. The classification results of the proposed method revealed that it required less time to learn than prominent models and had similar accuracy.
引用
收藏
页码：26496 / 26501
页数：6
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