Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/2366
Title: Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition
Authors: Xie, HB
Zheng, YP 
Guo, JY
Keywords: Mechanomyogram
Electromyogram
Muscle activity classification
Wavelet packet transform
Singular value decomposition
Prosthetic control
Issue Date: May-2009
Publisher: IOP Publishing
Source: Physiological measurement, May 2009, v. 30, no. 5, p. 441-457 How to cite?
Journal: Physiological measurement 
Abstract: Previous works have resulted in some practical achievements for mechanomyogram (MMG) to control powered prostheses. This work presents the investigation of classifying the hand motion using MMG signals for multifunctional prosthetic control. MMG is thought to reflect the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechanical noise sources such as a movement artifact are known to cause considerable interference to MMG, compromising the classification accuracy. To solve this noise problem, we proposed a new scheme to extract robust MMG features by the integration of the wavelet packet transform (WPT), singular value decomposition (SVD) and a feature selection technique based on distance evaluation criteria for the classification of hand motions. The WPT was first adopted to provide an effective time–frequency representation of non-stationary MMG signals. Then, the SVD and the distance evaluation technique were utilized to extract and select the optimal feature representing the hand motion patterns from the MMG time–frequency representation matrix. Experimental results of 12 subjects showed that four different motions of the forearm and hand could be reliably differentiated using the proposed method when two channels of MMG signals were used. Compared with three previously reported time–frequency decomposition methods, i.e. short-time Fourier transform, stationary wavelet transform and S-transform, the proposed classification system gave the highest average classification accuracy up to 89.7%. The results indicated that MMG could potentially serve as an alternative source of electromyogram for multifunctional prosthetic control using the proposed classification method.
URI: http://hdl.handle.net/10397/2366
ISSN: 0967-3334
DOI: 10.1088/0967-3334/30/5/002
Rights: © 2009 IOP Publishing Ltd. This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at DOI:10.1088/0967-3334/30/5/002
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