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 |
|---|---|
| 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 |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 2160-133X |
| ISSN (Electronic) | 2160-1348 |
Conference
| Conference | 2018 International Conference on Machine Learning and Cybernetics |
|---|---|
| Abbreviated title | ICMLC 2018 |
| Country | China |
| City | Chengdu |
| Period | 15/07/18 → 18/07/18 |
Links
Related information
ID: 12676100
