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A hybrid Cnn-Svm classifier for hand gesture recognition with surface Emg signals

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication2018 International Conference on Machine Learning and Cybernetics
Subtitle of host publicationICMLC
PublisherIEEE
Pages619-624
Number of pages6
ISBN (Electronic)978-1-5386-5214-5
ISBN (Print)978-1-5386-5215-2
DOIs
Publication statusPublished - 12 Nov 2018
Event2018 International Conference on Machine Learning and Cybernetics - http://www.icmlc.com/icmlc/welcome.html, Chengdu, China
Duration: 15 Jul 201818 Jul 2018

Publication series

NameIEEE ICMLC Proceedings Series
PublisherIEEE
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference2018 International Conference on Machine Learning and Cybernetics
Abbreviated titleICMLC 2018
CountryChina
CityChengdu
Period15/07/1818/07/18

Related information

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ID: 12676100