A Fast Classification System for Decoding of Human Hand Configurations Using Multi-Channel sEMG Signals

Title
A Fast Classification System for Decoding of Human Hand Configurations Using Multi-Channel sEMG Signals
Authors
박명수김기훈오상록
Issue Date
2011-09
Publisher
IEEE/RSJ International Conference on Intelligent Robots and Systems
Citation
, 4483-4487
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.
URI
http://pubs.kist.re.kr/handle/201004/40400
Appears in Collections:
KIST Publication > Conference Paper
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