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

conference contribution
posted on 2023-05-23, 08:18 authored by Khong, LMD, Timothy GaleTimothy Gale, Jiang, D, Jan OlivierJan Olivier, Ortiz-Catalan, M

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.

History

Publication title

Proceedings of the 6th Biomedical Engineering International Conference (BMEiCON2013)

Editors

C Pintavirooj

Pagination

6687665.1-5

ISBN

978-1-4799-1466-1

Department/School

School of Engineering

Publisher

IEEE

Place of publication

Krabi, Thailand

Event title

6th Biomedical Engineering International Conference (BMEiCON2013)

Event Venue

Krabi, Thailand

Date of Event (Start Date)

2013-10-23

Date of Event (End Date)

2013-10-25

Rights statement

Copyright 2013 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Diagnosis of human diseases and conditions

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