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Multi-layer perceptron training algorithms for pattern recognition of myoelectric signals


Khong, LMD and Gale, TJ and Jiang, D and Olivier, JC and Ortiz-Catalan, M, Multi-layer perceptron training algorithms for pattern recognition of myoelectric signals, Proceedings of the 6th Biomedical Engineering International Conference (BMEiCON2013), 23-25 October 2013, Krabi, Thailand, pp. 6687665.1-5. ISBN 978-1-4799-1466-1 (2013) [Refereed Conference Paper]

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Copyright 2013 IEEE

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DOI: doi:10.1109/BMEiCon.2013.6687665


A challenge in using myoelectric signals in control of motorised prostheses is achieving effective signal pattern recognition and robust classification of intended motions. In this paper, the performance of Matlab’s Multi-layer Perceptron (MLP) backpropogation training algorithms in motion classification were assessed. The test and evaluation platform used was "BioPatRec", a Matlab-based open-source prosthetic control development environment, together with algorithms sourced from Matlab’s neural network toolbox. The algorithms were used to interpret multielectrode myoelectric signals for motion classification, with the aim of finding the best performing algorithm and network model. The results showed that Matlab’s trainlm and trainrp algorithms could achieve a higher accuracy than other tested MLP training algorithms (94.13 ± 0.037% and 91.09 ± 0.047%, respectively). Discussion of these results investigates significant features to obtain the highest performance.

Item Details

Item Type:Refereed Conference Paper
Keywords:prosthetic control; pattern recognition;
Research Division:Engineering
Research Group:Biomedical engineering
Research Field:Biomedical engineering not elsewhere classified
Objective Division:Health
Objective Group:Clinical health
Objective Field:Diagnosis of human diseases and conditions
UTAS Author:Khong, LMD (Mr Le Minh Diep Khong)
UTAS Author:Gale, TJ (Dr Timothy Gale)
UTAS Author:Jiang, D (Dr Danchi Jiang)
UTAS Author:Olivier, JC (Professor JC Olivier)
ID Code:88764
Year Published:2013
Deposited By:Engineering
Deposited On:2014-02-14
Last Modified:2014-08-05

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