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%.
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页数：4
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