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Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network

Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network
Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network
The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles—Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantly
pattern recognition, EMG, ANFIS, neural network, prosthetic hand
1-15
Fariman, J.H.
82894837-3db2-4e79-ae2a-7fca36cffefc
Ahmad, S.A.
5e070c81-8901-4106-b386-93f09cd921d2
Marhaban, M.H.
7e202bc4-bae4-4def-beb7-1bb64ee8964e
Ghasab, M.A.J.
6d7eae6f-0942-4dce-8043-42080eb15464
Chappell, P.H.
2d2ec52b-e5d0-4c36-ac20-0a86589a880e
Fariman, J.H.
82894837-3db2-4e79-ae2a-7fca36cffefc
Ahmad, S.A.
5e070c81-8901-4106-b386-93f09cd921d2
Marhaban, M.H.
7e202bc4-bae4-4def-beb7-1bb64ee8964e
Ghasab, M.A.J.
6d7eae6f-0942-4dce-8043-42080eb15464
Chappell, P.H.
2d2ec52b-e5d0-4c36-ac20-0a86589a880e

Fariman, J.H., Ahmad, S.A., Marhaban, M.H., Ghasab, M.A.J. and Chappell, P.H. (2015) Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network. Intelligent Automation & Soft Computing, 1-15. (doi:10.1080/10798587.2015.1008735).

Record type: Article

Abstract

The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles—Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantly

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Ahmad Intell Auto Soft Compt 2015 AcceptedManuscript-IASC4175.pdf - Author's Original
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More information

e-pub ahead of print date: 12 February 2015
Keywords: pattern recognition, EMG, ANFIS, neural network, prosthetic hand
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 374618
URI: https://eprints.soton.ac.uk/id/eprint/374618
PURE UUID: 67b045c3-b89f-4dbd-ae92-ece02506c211

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Date deposited: 24 Feb 2015 14:00
Last modified: 17 Jul 2017 21:25

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