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Abstract
This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method.
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1155/2016/6481282
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
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Computational and Mathematical Methods in Medicine
no. 2016,
pages 1 - 7,
ISSN: 1748-670X - Language:
- English
- Publication year:
- 2016
- Bibliographic description:
- Pałkowski A., Redlarski G.: Basic Hand Gestures Classification Based on Surface Electromyography// Computational and Mathematical Methods in Medicine. -Vol. 2016, (2016), s.1-7
- DOI:
- Digital Object Identifier (open in new tab) 10.1155/2016/6481282
- Verified by:
- Gdańsk University of Technology
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