Surface EMG Signal Classification Using a Selective Mix of Higher Order Statistics

K. Nazarpour, A. R. Sharafat, S. M. P. Firoozabadi

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

Abstract

We describe a novel application of higher order statistics (HOS) for classifying surface electromyogram (sEMG) signals. We have followed seven approaches to identify discriminating signals representative of four primitive motions, i.e., elbow flexion/extension and forearm supination/pronation. The sequential forward selection (SFS) method is utilized to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information. The SFS selected the kurtosis of sEMG as well as its second order statistics as discriminating features. Our method is robust, and does not require additional computations as compared to existing efficient methods for providing higher rates of correct classification of sEMG, which make it useful in practical sEMG controlled prostheses
Original languageEnglish
Title of host publication2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4208-4211
Number of pages4
ISBN (Print)0-7803-8741-4
DOIs
Publication statusPublished - 10 Apr 2006
Event2005 IEEE Engineering in Medicine and Biology 27th Annual Conference - Shanghai, China
Duration: 17 Jan 200618 Jan 2006

Publication series

Name
PublisherIEEE
ISSN (Print)1094-687X
ISSN (Electronic)1558-4615

Conference

Conference2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
CountryChina
CityShanghai
Period17/01/0618/01/06

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