Pattern Recognition of Surface EMG Biological Signals by Means of Hilbert Spectrum and Fuzzy Clustering

  • Ruben-Dario Pinzon-Morales
  • Katherine-Andrea Baquero-Duarte
  • Alvaro-Angel Orozco-Gutierrez
  • Victor-Hugo Grisales-Palacio
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 696)


A novel method for hand movement pattern recognition from electromyography (EMG) biological signals is proposed. These signals are recorded by a three-channel data acquisition system using surface electrodes placed over the forearm, and then processed to recognize five hand movements: opening, closing, supination, flexion, and extension. Such method combines the Hilbert–Huang analysis with a fuzzy clustering classifier. A set of metrics, calculated from the time contour of the Hilbert Spectrum, is used to compute a discriminating three-dimensional feature space. The classification task in this feature-space is accomplished by a two-stage procedure where training cases are initially clustered with a fuzzy algorithm, and test cases are then classified applying a nearest-prototype rule. Empirical analysis of the proposed method reveals an average accuracy rate of 96% in the recognition of surface EMG signals.


Fuzzy Cluster Empirical Mode Decomposition Intrinsic Mode Function SEMG Signal Hilbert Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Ruben-Dario Pinzon-Morales
    • 1
  • Katherine-Andrea Baquero-Duarte
  • Alvaro-Angel Orozco-Gutierrez
  • Victor-Hugo Grisales-Palacio
  1. 1.Faculty of Engineering, Research Group on Control and InstrumentationUniversidad Tecnologica de PereiraPereiraColombia

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