An Experimental Study of EMG Signal Features for Motion Discriminations Using Support Vector Machine

Takuya Shinozaki; Kazuki Kojima; Satoshi Ito; Minoru Sasaki

doi:10.4028/www.scientific.net/AEF.2-3.249

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HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 2-3An Experimental Study of EMG Signal Features for...

An Experimental Study of EMG Signal Features for Motion Discriminations Using Support Vector Machine

Abstract:

This paper treats a discrimination problem of wrist/hand motion patterns from EMG signal. We examined which of the following signal features was appropriate: raw signal, integrated signal (IEMG), the max frequency component, power spectrum or rising voltage level. For the discrimination algorism, a Support Vector Machine (SVM) was introduced. As a result, around 80% discrimination rate was accomplished from integrated signal, power spectrum and rising voltage level. The IEMG signal scored the highest 83.3% discrimination rate.

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Periodical:

Advanced Engineering Forum (Volumes 2-3)

Pages:

249-252

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.2-3.249

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Online since:

December 2011

Authors:

Takuya Shinozaki, Kazuki Kojima, Satoshi Ito, Minoru Sasaki

Keywords:

Signal Processing, Support Vector Machine (SVM), Surface EMG

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Creative Commons CC BY 4.0

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