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


Citation

Fariman, Hessam Jahani and Ahmad, Siti Anom and Marhaban, Mohammad Hamiruce and Ghasab, Mohammad Ali Jan and Chappell, Paul H. (2015) Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network. Intelligent Automation & Soft Computing, 21 (4). pp. 559-573. ISSN 1079-8587; ESSN: 2326-005X

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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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1080/10798587.2015.1008735
Publisher: Taylor & Francis
Keywords: Pattern recognition; EMG; ANFIS; Neural network; Prosthetic hand
Depositing User: Nabilah Mustapa
Date Deposited: 03 Aug 2017 04:53
Last Modified: 03 Aug 2017 04:53
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/10798587.2015.1008735
URI: http://psasir.upm.edu.my/id/eprint/56579
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