A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses
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 s...
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| Published in | IEEE transactions on biomedical engineering Vol. 52; no. 11; pp. 1801 - 1811 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.11.2005
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Chan, A.D.C. Yonghong Huang Hudgins, B. Englehart, K.B. |
| Author_xml | – sequence: 1 surname: Yonghong Huang fullname: Yonghong Huang organization: Dept. of Electr., Univ. of New Brunswick, Fredericton, NB, Canada – sequence: 2 givenname: K.B. surname: Englehart fullname: Englehart, K.B. email: kengleha@unb.ca organization: Dept. of Electr., Univ. of New Brunswick, Fredericton, NB, Canada – sequence: 3 givenname: B. surname: Hudgins fullname: Hudgins, B. organization: Dept. of Electr., Univ. of New Brunswick, Fredericton, NB, Canada – sequence: 4 givenname: A.D.C. surname: Chan fullname: Chan, A.D.C. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16285383$$D View this record in MEDLINE/PubMed |
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| CODEN | IEBEAX |
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| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005 |
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| References | ref13 Lawrence (ref2) 1973 ref15 ref14 ref20 ref11 Englehart (ref9) ref21 Finley (ref1) 1967; 48 Leowinata (ref10) ref17 ref16 ref19 ref18 ref8 ref4 Gallant (ref12) 1993 ref6 ref5 Gallant (ref7) 1993 Lyman (ref3) 1976 |
| References_xml | – ident: ref6 doi: 10.1016/S1350-4533(99)00066-1 – volume-title: 16th Annual Congress of the International Society Electrophysiology and Kinesiology ident: ref10 article-title: A multifunction myoelectric control strategy using an array of electrodes – ident: ref11 doi: 10.1109/IEMBS.2003.1279711 – ident: ref17 doi: 10.1109/89.365379 – ident: ref13 doi: 10.1109/10.914793 – volume-title: An approach to myoelectric control using a self-organizing neural network for feature extraction year: 1993 ident: ref7 – volume-title: Proc. World Congr. Neural Networks ident: ref9 article-title: Classification of transient myoelectric signals using a dynamic feedforward neural network – ident: ref19 doi: 10.1016/0167-6393(95)00009-D – ident: ref21 doi: 10.3233/TAD-2003-15205 – ident: ref8 doi: 10.1109/70.538982 – volume: 48 start-page: 598 year: 1967 ident: ref1 article-title: Myocoder studies of multiple myocoder response publication-title: Arch. Phys. Med. Rehabil. – ident: ref16 doi: 10.1109/ICASSP.2002.5745552 – ident: ref14 doi: 10.1109/TBME.2003.813539 – ident: ref4 doi: 10.1109/10.204774 – start-page: 184 volume-title: Bull. Prosthetics Res. year: 1976 ident: ref3 article-title: Fundamental and applied research related to the design and devolopment of upper-limb externally powered prostheses – volume-title: An approach to myoelectric control using a self-organizing neural network for feature extraction year: 1993 ident: ref12 – ident: ref18 doi: 10.1006/dspr.1999.0361 – start-page: 47 volume-title: Advances in External Control of Human Extremities year: 1973 ident: ref2 article-title: Experiences with a multifunctional hand prosthesis controlled by myoelectric patterns – ident: ref5 doi: 10.1109/iembs.1998.745109 – ident: ref20 doi: 10.1162/089976600300015439 – ident: ref15 doi: 10.1109/iembs.2003.1280510 |
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| Snippet | This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The... |
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| SubjectTerms | Algorithms Artificial Intelligence Classification Electromyography - methods EMG Gaussian mixture model Joint Prosthesis Linear discriminant analysis Models, Biological Models, Statistical Movement - physiology Multi-layer neural network Multilayer perceptrons Muscle Contraction - physiology myoelectric signals Neural networks Normal Distribution pattern recognition Pattern Recognition, Automated - methods Power system modeling prosthesis Prosthesis Design - methods Prosthetics Robustness Spatial databases Therapy, Computer-Assisted - methods Time domain analysis Upper Extremity - physiology Voting |
| Title | A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses |
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