A hybrid Cnn-Svm classifier for hand gesture recognition with surface Emg signals
Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language | English |
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Title of host publication | 2018 International Conference on Machine Learning and Cybernetics |
Subtitle of host publication | ICMLC |
Publisher | IEEE |
Pages | 619-624 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-5386-5214-5 |
ISBN (Print) | 978-1-5386-5215-2 |
DOIs | |
Publication status | Published - 12 Nov 2018 |
Event | 2018 International Conference on Machine Learning and Cybernetics - http://www.icmlc.com/icmlc/welcome.html, Chengdu, China Duration: 15 Jul 2018 → 18 Jul 2018 |
Publication series
Name | IEEE ICMLC Proceedings Series |
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Publisher | IEEE |
ISSN (Print) | 2160-133X |
ISSN (Electronic) | 2160-1348 |
Conference
Conference | 2018 International Conference on Machine Learning and Cybernetics |
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Abbreviated title | ICMLC 2018 |
Country | China |
City | Chengdu |
Period | 15/07/18 → 18/07/18 |
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ID: 12676100