A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses

Hdl Handle:
http://hdl.handle.net/10147/203780
Title:
A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses
Authors:
Huang, Y.; Englehart, K.B.; Hudgins, B.; Chan, A.D.C.
Citation:
A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses 2005, 52 (11):1801 IEEE Transactions on Biomedical Engineering
Journal:
IEEE Transactions on Biomedical Engineering
Issue Date:
Nov-2005
URI:
http://hdl.handle.net/10147/203780
DOI:
10.1109/TBME.2005.856295
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1519588
Item Type:
Article
Language:
en
Description:
This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load.
ISSN:
0018-9294

Full metadata record

DC FieldValue Language
dc.contributor.authorHuang, Y.en
dc.contributor.authorEnglehart, K.B.en
dc.contributor.authorHudgins, B.en
dc.contributor.authorChan, A.D.C.en
dc.date.accessioned2012-01-19T16:37:05Z-
dc.date.available2012-01-19T16:37:05Z-
dc.date.issued2005-11-
dc.identifier.citationA Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses 2005, 52 (11):1801 IEEE Transactions on Biomedical Engineeringen
dc.identifier.issn0018-9294-
dc.identifier.doi10.1109/TBME.2005.856295-
dc.identifier.urihttp://hdl.handle.net/10147/203780-
dc.descriptionThis paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load.en
dc.language.isoenen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1519588en
dc.titleA Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prosthesesen
dc.typeArticleen
dc.identifier.journalIEEE Transactions on Biomedical Engineeringen
dc.description.provinceMunster-
All Items in Lenus, The Irish Health Repository are protected by copyright, with all rights reserved, unless otherwise indicated.