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A machine learning based method for classification of fractal features of forearm sEMG using Twin Support Vector Machines

Poosapadi Arjunan, S, Kumar, D and Naik, G 2010, 'A machine learning based method for classification of fractal features of forearm sEMG using Twin Support Vector Machines', in Ricardo L. Armentano, (ed.) Proceedings of the 32nd Annual International Conference of the IEEE EMBS, USA, Aug 31 - Sep 4, 2010, pp. 4821-4824.

Document type: Conference Paper
Collection: Conference Papers

Title A machine learning based method for classification of fractal features of forearm sEMG using Twin Support Vector Machines
Author(s) Poosapadi Arjunan, S
Kumar, D
Naik, G
Year 2010
Conference name 32nd Annual International Conference of the IEEE EMBS
Conference location USA
Conference dates Aug 31 - Sep 4, 2010
Proceedings title Proceedings of the 32nd Annual International Conference of the IEEE EMBS
Editor(s) Ricardo L. Armentano,
Publisher IEEE
Place of publication USA
Start page 4821
End page 4824
Total pages 4
Abstract Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors.These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF).
Subjects Signal Processing
Biomedical Engineering not elsewhere classified
Copyright notice © 2010 IEEE
ISBN 9781424441242
 
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