Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration

Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on... Neural Process Lett https://doi.org/10.1007/s11063-018-9862-0 Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration 1 1 1 Li Ge · Li-Juan Ge · Jing Hu © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract On the basis of analysing the characteristics of hand movement surface electrocar- diogram electromyogram (sEMG) signals, we propose a feature extraction and classification method for hand movement sEMG signals based on a multi-method integration combin- ing the wavelet, fractal and statistics methods. To start, the hand movement sEMG signals are de-noised by using the wavelet transform, the de-noised and reconstructed signals are decomposed, and the average high frequency coefficients in each scale space are calculated to constitute the feature vectors as the first part of the hand movement sEMG signals classifica- tion features. Next, according to the characteristics of hand movement sEMG signals and the classification needs, we analyse the multi-fractal spectrum of the de-noised and reconstructed signals at multiple scales and extract the relevant parameters of multi-fractal spectrum as the second part of the hand movement sEMG signals classification features. Then, according to the characteristics of hand movement sEMG signals, we extract the relevant statistical characteristics of sEMG http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Processing Letters Springer Journals

Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
D.O.I.
10.1007/s11063-018-9862-0
Publisher site
See Article on Publisher Site

Abstract

Neural Process Lett https://doi.org/10.1007/s11063-018-9862-0 Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration 1 1 1 Li Ge · Li-Juan Ge · Jing Hu © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract On the basis of analysing the characteristics of hand movement surface electrocar- diogram electromyogram (sEMG) signals, we propose a feature extraction and classification method for hand movement sEMG signals based on a multi-method integration combin- ing the wavelet, fractal and statistics methods. To start, the hand movement sEMG signals are de-noised by using the wavelet transform, the de-noised and reconstructed signals are decomposed, and the average high frequency coefficients in each scale space are calculated to constitute the feature vectors as the first part of the hand movement sEMG signals classifica- tion features. Next, according to the characteristics of hand movement sEMG signals and the classification needs, we analyse the multi-fractal spectrum of the de-noised and reconstructed signals at multiple scales and extract the relevant parameters of multi-fractal spectrum as the second part of the hand movement sEMG signals classification features. Then, according to the characteristics of hand movement sEMG signals, we extract the relevant statistical characteristics of sEMG

Journal

Neural Processing LettersSpringer Journals

Published: May 28, 2018

References

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