The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis
Abstract
Accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. The aim of this paper is to classify myoelectric signals using new fuzzy clustering neural network (NN) architectures to control multifunction prostheses. This paper presents a comparative study of the classification accuracy of myoelectric signals using multilayered perceptron NN using back-propagation, conic section function NN, and new fuzzy clustering NNs (FCNNs). The myoelectric signals considered are used in classifying six upper-limb movements: elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster. This method has the potential of being very efficient in real-time applications.
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Cites
Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes
Graupe, D., & Cline, W. K. (1975) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1975.5408479 · Cited by 354
Cited by
Myoelectric control systems—A survey
Asghari Oskoei, M., & Hu, H. (2007) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.07.009 · Cited by 1,244
Myoelectric forearm prostheses: State of the art from a user-centered perspective
Peerdeman, B., Boere, D., Witteveen, H., Huis in `tVeld, R., Hermens, H., Stramigioli, S., Rietman, H., Veltink, P., & Misra, S. (2011) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2010.08.0161 · Cited by 469
A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control
Ajiboye, A. B., & Weir, R. F. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2005.847357 · Cited by 415
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., & Geng, W. (2018) · PLOS ONE · doi:10.1371/journal.pone.0206049 · Cited by 398
Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T. J., Naik, G. R., Gunawardana, U., et al. (2019) · Sensors · doi:10.3390/s19204596 · Cited by 332
Chu, J.-U., Moon, I., Lee, Y.-J., Kim, S.-K., & Mun, M.-S. (2007) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2007.897262 · Cited by 299
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Özbay, Y., Ceylan, R., & Karlik, B. (2006) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2005.01.006 · Cited by 254
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Metadata
- Authors
- Karlik, B., Tokhi, M. O., & Alci, M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2003-11
- Volume
- 50
- Issue
- 11
- Pages
- 1255-1261
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2153727374
- Cited by
- 198
- References
- 1
- FWCI
- 5.12
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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