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| Content Provider | Springer Nature Link |
|---|---|
| Author | Jahani Fariman, H. Ahmad, Siti A. Hamiruce Marhaban, M. Alijan Ghasab, M. Chappell, Paul H. |
| Copyright Year | 2015 |
| Abstract | 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. |
| Starting Page | 85 |
| Ending Page | 102 |
| Page Count | 18 |
| File Format | |
| ISSN | 01589938 |
| Journal | Australasian Physics & Engineering Sciences in Medicine |
| Volume Number | 39 |
| Issue Number | 1 |
| e-ISSN | 18795447 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2015-11-18 |
| Publisher Place | Dordrecht |
| Access Restriction | Subscribed |
| Subject Keyword | Pattern recognition EMG Adaptive resonance theory Neural network Prosthetic hand Biomedicine general Biophysics and Biological Physics Medical and Radiation Physics Biomedical Engineering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging Physics and Astronomy Biophysics Biomedical Engineering |
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