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Online learning method using support vector machine for surface-electromyogram recognition

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Abstract

Research surface electromyogram (s-EMG) signal recognition using neural networks is a method which identifies the relation between s-EMG patterns. However, it is not sufficiently satisfying for the user because s-EMG signals change according to muscle wasting or to changes in the electrode position, etc. A support vector machine (SVM) is one of the most powerful tools for solving classification problems, but it does not have an online learning technique. In this article, we propose an online learning method using SVM with a pairwise coupling technique for s-EMG recognition. We compared its performance with the original SVM and a neural network. Simulation results showed that our proposed method is better than the original SVM.

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Authors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, 1-1 Gakuken kibanadai-nishi, Miyazaki, 889-2192, Japan

    Shuji Kawano, Dai Okumura, Hiroki Tamura, Hisasi Tanaka & Koichi Tanno

Authors
  1. Shuji Kawano
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  2. Dai Okumura
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  3. Hiroki Tamura
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  4. Hisasi Tanaka
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  5. Koichi Tanno
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Corresponding author

Correspondence to Shuji Kawano.

Additional information

This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008

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Kawano, S., Okumura, D., Tamura, H. et al. Online learning method using support vector machine for surface-electromyogram recognition. Artif Life Robotics 13, 483–487 (2009). https://doi.org/10.1007/s10015-008-0607-4

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  • DOI: https://doi.org/10.1007/s10015-008-0607-4

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