EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction

  • Bita Mokhlesabadifarahani
  • Vinit Kumar Gunjan

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the Forensic and Medical Bioinformatics book sub series (BRIEFSFOMEBI)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
    Pages 1-9
  3. Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
    Pages 11-20
  4. Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
    Pages 21-26
  5. Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
    Pages 27-27
  6. Back Matter
    Pages 29-35

About this book


Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.


Electromyography (EMG) Feature Extraction Fuzzy Network Musculoskeletal Disorders Neuro-fuzzy Classifiers Neuro-muscular Disorders Neuro-rehabilitation Orthopedic Rehabilitation

Authors and affiliations

  • Bita Mokhlesabadifarahani
    • 1
  • Vinit Kumar Gunjan
    • 2
  1. 1.Biomedical Engineering DepartmentAmirkabir University of TechnologyTehranIran
  2. 2.Annamacharya Institute of Technology and Sciences (AITS)RajampetIndia

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2015
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-287-319-4
  • Online ISBN 978-981-287-320-0
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
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