A fast classification system for decoding of human hand configurations using multi-channel sEMG signals | IEEE Conference Publication | IEEE Xplore

A fast classification system for decoding of human hand configurations using multi-channel sEMG signals


Abstract:

This paper proposes a novel fast classification system consisting of feature extraction and classifier to decode human hand configurations from multi-channel surface elec...Show More

Abstract:

This paper proposes a novel fast classification system consisting of feature extraction and classifier to decode human hand configurations from multi-channel surface electromyogram (sEMG) signals that allows real-time classification of human movement intention as well as prothesis control. In order to enhance the learning speed and the performance of the classifier, we used a supervised feature extraction method (called class-augmented principal component analysis) and a fast learning classifier (called extreme learning machine). Experimental results show that the proposed classification system quickly learns and decodes the human hand configuration with about 92% accuracy.
Date of Conference: 25-30 September 2011
Date Added to IEEE Xplore: 05 December 2011
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Conference Location: San Francisco, CA, USA

References

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