Hand movements classification for myoelectric control system using adaptive resonance theory
This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47437/1/Hand%20movements%20classification%20for%20myoelectric%20control%20system%20using%20adaptive%20resonance%20theory.pdf |
| _version_ | 1796974788952981504 |
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| author | Fariman, Hessam Jahani Ahmad, Siti Anom Marhaban, Mohammad Hamiruce Ghasab, Mohammad Ali Jan Chappell, Paul H. |
| author_facet | Fariman, Hessam Jahani Ahmad, Siti Anom Marhaban, Mohammad Hamiruce Ghasab, Mohammad Ali Jan Chappell, Paul H. |
| author_sort | Fariman, Hessam Jahani |
| collection | UPM |
| description | This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins’ multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09 %) but also a significantly improved computation time. |
| first_indexed | 2024-03-06T09:02:05Z |
| format | Article |
| id | upm.eprints-47437 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:02:05Z |
| publishDate | 2016 |
| publisher | Springer |
| record_format | dspace |
| spelling | upm.eprints-474372016-05-20T01:21:14Z http://psasir.upm.edu.my/id/eprint/47437/ Hand movements classification for myoelectric control system using adaptive resonance theory Fariman, Hessam Jahani Ahmad, Siti Anom Marhaban, Mohammad Hamiruce Ghasab, Mohammad Ali Jan Chappell, Paul H. This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins’ multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09 %) but also a significantly improved computation time. Springer 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47437/1/Hand%20movements%20classification%20for%20myoelectric%20control%20system%20using%20adaptive%20resonance%20theory.pdf Fariman, Hessam Jahani and Ahmad, Siti Anom and Marhaban, Mohammad Hamiruce and Ghasab, Mohammad Ali Jan and Chappell, Paul H. (2016) Hand movements classification for myoelectric control system using adaptive resonance theory. Australasian Physical & Engineering Sciences in Medicine, 39 (1). pp. 85-102. ISSN 0158-9938; ESSN: 1879-5447 http://link.springer.com/article/10.1007%2Fs13246-015-0399-5 10.1007/s13246-015-0399-5 |
| spellingShingle | Fariman, Hessam Jahani Ahmad, Siti Anom Marhaban, Mohammad Hamiruce Ghasab, Mohammad Ali Jan Chappell, Paul H. Hand movements classification for myoelectric control system using adaptive resonance theory |
| title | Hand movements classification for myoelectric control system using adaptive resonance theory |
| title_full | Hand movements classification for myoelectric control system using adaptive resonance theory |
| title_fullStr | Hand movements classification for myoelectric control system using adaptive resonance theory |
| title_full_unstemmed | Hand movements classification for myoelectric control system using adaptive resonance theory |
| title_short | Hand movements classification for myoelectric control system using adaptive resonance theory |
| title_sort | hand movements classification for myoelectric control system using adaptive resonance theory |
| url | http://psasir.upm.edu.my/id/eprint/47437/1/Hand%20movements%20classification%20for%20myoelectric%20control%20system%20using%20adaptive%20resonance%20theory.pdf |
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