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Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Spherical classification of wavelet transformed EMG intensity patterns.

Vinzenz von Tscharner1

  • 1Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada T2N 1N4. vincent@kin.ucalgary.ca

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology
|August 20, 2008
PubMed
Summary

Multi-Muscle Patterns (MMPs) from electromyograms can be classified by analyzing their distribution in pattern space. This method accurately distinguishes between barefoot and shod runners based on their MMPs, achieving over 80% recognition.

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Extraction of the EPP Component from the Surface EMG
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Extraction of the EPP Component from the Surface EMG
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Published on: December 16, 2009

Area of Science:

  • Biomechanics
  • Signal Processing
  • Data Analysis

Background:

  • Electromyograms (EMGs) capture muscle electrical activity.
  • Wavelet transforms can analyze EMG signals, creating Multi-Muscle Patterns (MMPs).
  • MMPs represent simultaneous muscle activity in time/frequency space.

Purpose of the Study:

  • To investigate the distribution of MMPs in pattern space.
  • To classify MMPs based on their distribution properties.
  • To test if MMP distributions can differentiate between barefoot and shod runners.

Main Methods:

  • EMG signals were processed using wavelet transforms to generate MMPs.
  • MMPs were mapped as points in a whitened Euclidean vector space (pattern space).
  • The distribution of MMP points within a sphere-like domain was analyzed.

Main Results:

  • MMP distributions were found to reside within predicted sphere-like domains in pattern space.
  • The distribution spheres for barefoot and shod runners were sufficiently separated for classification.
  • Classification accuracy based on spherical features exceeded 80%.

Conclusions:

  • MMP distributions in pattern space are characteristic of specific conditions (e.g., barefoot vs. shod).
  • Classification using spherical distribution features offers superior discrimination compared to distance-based methods.
  • This approach provides a robust method for classifying gait conditions using EMG data.