The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb
Abstract
This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
A robust, real-time control scheme for multifunction myoelectric control
Englehart, K., & Hudgins, B. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.813539 · Cited by 1,743
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
Self-citationAu, A. T. C., & Kirsch, R. F. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895950 · Cited by 182
Cited by
Feature reduction and selection for EMG signal classification
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.102 · Cited by 1,542
Huang, H., Zhang, F., Hargrove, L. J., Dou, Z., Rogers, D. R., & Englehart, K. B. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161671 · Cited by 542
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
Du, Y., Jin, W., Wei, W., Hu, Y., & Geng, W. (2017) · Sensors · doi:10.3390/s17030458 · Cited by 363
EMG Pattern Recognition in the Era of Big Data and Deep Learning
Phinyomark, A., & Scheme, E. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030021 · Cited by 272
Ortiz-Catalan, M., Brånemark, R., & Håkansson, B. (2013) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-8-11 · Cited by 232
Metadata
- Authors
- Oskoei, M. A., & Hu, H.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2008-08
- Volume
- 55
- Issue
- 8
- Pages
- 1956-1965
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2134408600
- Cited by
- 829
- References
- 5
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- closed
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