NDLI: Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal

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Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal

Proceedings of the National Academy of Sciences, India Section A: Physical Sciences : Volume 84, Issue 2, June 2014

Proceedings of the National Academy of Sciences, India Section A: Physical Sciences : Volume 84, Issue 1, March 2014

Proceedings of the National Academy of Sciences, India Section A: Physical Sciences : Volume 83

Proceedings of the National Academy of Sciences, India Section A: Physical Sciences : Volume 82

Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal

Content Provider	Springer Nature Link
Author	Hui, Jiang Al Omari, Firas Mei, Congli Liu, Guohai
Copyright Year	2014
Abstract	In this manuscript, eight hand motions were classified using ten different extracted features from sEMG signals. These signals were collected from four different muscles placed on the forearm. It was found out that the performance of a classifier was improved through the implementation of more than one feature. We tested two feature combinations; the classification accuracy rate of 94 % was achieved using linear discriminant analysis (LDA) based on wavelength (WAVE), Wilson amplitude (WAMP), and root mean square combination. The performance of four wavelet families was tested to select the proper wavelet family that leads to highest classification rate. Our experimental results demonstrate that the highest average classification accuracy was 95 % achieved by implementing general neural network (GRNN) classification method based on energy of wavelet coefficients (using Sym4 family). Moreover, this study investigated the performance of three SVM-kernel functions (support vector machine) and found that polynomial function is the optimal choice in most cases. The highest achieved classification accuracy was 93 % using extracted wavelet coefficients.
Starting Page	473
Ending Page	480
Page Count	8
File Format	PDF
ISSN	03698203
Journal	Proceedings of the National Academy of Sciences, India Section A: Physical Sciences
Volume Number	84
Issue Number	3
e-ISSN	22501762
Language	English
Publisher	Springer India
Publisher Date	2014-06-20
Publisher Place	India
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bio-signal processing Applied and Technical Physics Wavelet analysis Atomic, Molecular, Optical and Plasma Physics Quantum Physics Pattern recognition Neural network Human–computer interface Artificial intelligence Physics
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy Biochemistry, Genetics and Molecular Biology

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Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal

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