A HYBRID CNN-SVM CLASSIFIER FOR HAND GESTURE RECOGNITION WITH SURFACE EMG SIGNALS

被引：0

作者：

Chen, Hongfeng ^{[1
]}

Tong, Runze ^{[1
]}

Chen, Minjie ^{[1
]}

Fang, Yinfeng ^{[2
]}

Liu, Honghai ^{[3
]}

机构：

[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou, Peoples R China

[2] Univ Portsmouth, Sch Comp, Portsmouth, Hants, England

[3] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2 | 2018年

关键词：

CNN; SVM; Surface EMG; Hand motion; Features; ROBUST;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A synthetic approach was proposed to improve the recognition accuracy. Different with the traditional feature extractors, this study used a convolutional neural network (CNN) to automatically extract characteristics from the input of raw EMG image. Then, a Support Vector Machine (SVM) classifier was employed to identify the hand motions. Our experiments showed that the proposed method achieved the accuracy around 2.5% higher than the use of CNN only, and about 9.7% higher than the use of traditional method (i.e. the use of time domain feature and a SVM classifier). Both inter-subject and inter-session tests demonstrated the robustness of the CNN-based feature.

引用

页码：619 / 624

页数：6

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