Name
Title | ||
|---|---|---|
Online learning method using support vector machine for surface-electromyogram recognition |
Abstract | ||
|---|---|---|
Research surface electromyogram (s-EMG) signal recognition using neural networks is a method which identifies the relation
between s-EMG patterns. However, it is not sufficiently satisfying for the user because s-EMG signals change according to
muscle wasting or to changes in the electrode position, etc. A support vector machine (SVM) is one of the most powerful tools
for solving classification problems, but it does not have an online learning technique. In this article, we propose an online
learning method using SVM with a pairwise coupling technique for s-EMG recognition. We compared its performance with the original
SVM and a neural network. Simulation results showed that our proposed method is better than the original SVM. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10015-008-0607-4 | Artificial Life and Robotics |
Keywords | DocType | Volume |
surface electromyogram · support vector machine · neural network · pattern classifi cation problem,support vector machine,neural network,satisfiability | Journal | 13 |
Issue | ISSN | Citations |
2 | 1614-7456 | 8 |
PageRank | References | Authors |
0.60 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuji Kawano | 1 | 10 | 1.02 |
| Dai Okumura | 2 | 8 | 0.60 |
| Hiroki Tamura | 3 | 72 | 21.29 |
| Hisasi Tanaka | 4 | 8 | 0.60 |
| Koichi Tanno | 5 | 57 | 22.05 |