Hand Gesture Recognition Using a Radar Echo I-Q Plot and a Convolutional Neural Network

被引：33

作者：

Sakamoto, Takuya ^{[1
,2
,3
]}

Gao, Xiaomeng ^{[4
,5
,6
]}

Yavari, Ehsan ^{[4
]}

Rahman, Ashikur ^{[1
,7
]}

Boric-Lubecke, Olga ^{[1
]}

Lubecke, Victor M. ^{[1
]}

机构：

[1] Univ Hawaii Manoa, Dept Elect Engn, Honolulu, HI 96822 USA

[2] Univ Hyogo, Grad Sch Engn, Himeji, Hyogo 6712280, Japan

[3] Kyoto Univ, Grad Sch Informat, Kyoto 6068501, Japan

[4] Adnoviv LLC, Honolulu, HI 96822 USA

[5] Univ Calif Davis, Davis, CA 95616 USA

[6] Cardiac Mot LLC, Sacramento, CA 95817 USA

[7] Aptiv PLC, Kokomo, IN 46902 USA

来源：

IEEE SENSORS LETTERS | 2018年 / 2卷 / 03期

基金：

日本学术振兴会;

关键词：

Sensor signals processing; gesture recognition; machine learning; neural network; radar;

D O I：

10.1109/LSENS.2018.2866371

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose a hand gesture recognition technique using a convolutional neural network applied to radar echo inphase/quadrature (I/Q) plot trajectories. The proposed technique is demonstrated to accurately recognize six types of hand gestures for ten participants. The system consists of a low-cost 2.4-GHz continuous-wave monostatic radar with a single antenna. The radar echo trajectories are converted to low-resolution images and are used for the training and evaluation of the proposed technique. Results indicate that the proposed technique can recognize hand gestures with average accuracy exceeding 90%.

引用

页数：4

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