Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control

Hong Bo Xie, Yongping Zheng, Jing Yi Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

107 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)441-457
Number of pages17
JournalPhysiological Measurement
Volume30
Issue number5
DOIs
Publication statusPublished - 4 Nov 2009

Keywords

  • Electromyogram
  • Mechanomyogram
  • Muscle activity classification
  • Prosthetic control
  • Singular value decomposition
  • Wavelet packet transform

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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