EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction [electronic resource] / by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan.

By:

Mokhlesabadifarahani, Bita [author.]

Contributor(s):

Series: SpringerBriefs in Applied Sciences and TechnologyPublisher: Singapore : Springer Singapore : Imprint: Springer, 2015Description: XV, 35 p. 17 illus., 13 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9789812873200

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

610.28 23

LOC classification:

R856-857

Online resources:

Click here to access online

Contents:

Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.

In: Springer eBooksSummary: 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.

Item type:

eBooks

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Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.

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.