SEMG classification for upper-limb prosthesis control using higher order statistics

Alireza Khadivi, Kianoush Nazarpour, Hamid Soltanain Zadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages385-388
Number of pages4
Volume5
ISBN (Print)0-7803-8874-7
DOIs
Publication statusPublished - 9 May 2005
EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05) - Philadelphia, United States
Duration: 18 Mar 200523 Mar 2005

Publication series

Name
PublisherIEEE
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05)
CountryUnited States
CityPhiladelphia
Period18/03/0523/03/05

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