Name
Abstract | ||
|---|---|---|
The aim of this paper is to present application of higher order statistics for Surface Electromyogram (sEMG) signal pattern classification. The new pattern recognition algorithm exploits a multilayer perceptron (MLP) as the classifier and the feature vector is a combination of cumulants of the second-, third- and fourth- orders and Integral of Absolute (IAV) of two channel sEMG stationary segments. The detected sEMG signals are used in classifying four upper-limb primitive motions, namely, elbow flexion (F), elbow extension (E), wrist supination (S) and wrist pronation (P). The simulation results illustrate the considerable accuracy of the proposed framework in sEMG pattern recognition. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1416321 | 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING |
Keywords | Field | DocType |
cumulant,biomedical engineering,feature vector,multilayer perceptron,pattern recognition,feature extraction,cumulants | Feature vector,Elbow,Wrist,Pattern recognition,Motion detection,Computer science,Higher-order statistics,Speech recognition,Feature extraction,Multilayer perceptron,Artificial intelligence,Classifier (linguistics) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.74 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Khadivi | 1 | 12 | 1.71 |
| Kianoush Nazarpour | 2 | 75 | 19.08 |
| Hamid Soltanian-Zadeh | 3 | 613 | 84.11 |