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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/56579/1/Simple%20and%20computationally%20efficient%20movement%20classification%20approach%20for%20EMG-controlled%20prosthetic%20hand%20ANFIS%20vs.%20artificial%20neural%20network.pdf |
| _version_ | 1796976507733671936 |
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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 | 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. |
| first_indexed | 2024-03-06T09:26:49Z |
| format | Article |
| id | upm.eprints-56579 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:26:49Z |
| publishDate | 2015 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | upm.eprints-565792017-08-03T04:53:00Z http://psasir.upm.edu.my/id/eprint/56579/ Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network Fariman, Hessam Jahani Ahmad, Siti Anom Marhaban, Mohammad Hamiruce Ghasab, Mohammad Ali Jan Chappell, Paul H. 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. Taylor & Francis 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/56579/1/Simple%20and%20computationally%20efficient%20movement%20classification%20approach%20for%20EMG-controlled%20prosthetic%20hand%20ANFIS%20vs.%20artificial%20neural%20network.pdf 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 http://www.tandfonline.com/doi/abs/10.1080/10798587.2015.1008735 10.1080/10798587.2015.1008735 |
| spellingShingle | Fariman, Hessam Jahani Ahmad, Siti Anom Marhaban, Mohammad Hamiruce Ghasab, Mohammad Ali Jan Chappell, Paul H. Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network |
| title | Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network |
| title_full | Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network |
| title_fullStr | Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network |
| title_full_unstemmed | Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network |
| title_short | Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network |
| title_sort | simple and computationally efficient movement classification approach for emg controlled prosthetic hand anfis vs artificial neural network |
| url | http://psasir.upm.edu.my/id/eprint/56579/1/Simple%20and%20computationally%20efficient%20movement%20classification%20approach%20for%20EMG-controlled%20prosthetic%20hand%20ANFIS%20vs.%20artificial%20neural%20network.pdf |
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