Title :
SEMG classification for upper-limb prosthesis control using higher order statistics
Author :
Khadivi, Alireza ; Nazarpour, Kianoush ; Zadeh, Hamid Soltanain
Author_Institution :
Dept. of Electr. & Comput. Eng., Tehran Univ., Iran
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.
Keywords :
artificial limbs; electromyography; feature extraction; higher order statistics; multilayer perceptrons; pattern classification; MLP; cumulants; elbow extension; elbow flexion; feature extraction; higher order statistics; integral of absolute; multilayer perceptron; neuromuscular activity; sEMG classification; sEMG pattern recognition; stationary segment integral of absolute; surface electromyogram signal pattern classification; upper-limb primitive motions; upper-limb prosthesis control; wrist pronation; wrist supination; Biomedical engineering; Elbow; Feature extraction; Higher order statistics; Motion detection; Muscles; Pattern classification; Pattern recognition; Prosthetics; Wrist;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1416321