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EMG signals characterization in thre...

Gunjan, Vinit Kumar.

EMG signals characterization in three states of contraction by fuzzy network and feature extraction

Record Type:	Electronic resources : Monograph/item
Title/Author:	EMG signals characterization in three states of contraction by fuzzy network and feature extractionby Bita Mokhlesabadifarahani, Vinit Kumar Gunjan.
Author:	Mokhlesabadifarahani, Bita.
other author:	Gunjan, Vinit Kumar.
Published:	Singapore :Springer Singapore :2015.
Description:	xv, 35 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Signal processingDigital techniques.
Online resource:	http://dx.doi.org/10.1007/978-981-287-320-0
ISBN:	9789812873200 (electronic bk.)

EMG signals characterization in three states of contraction by fuzzy network and feature extraction
Mokhlesabadifarahani, Bita.

EMG signals characterization in three states of contraction by fuzzy network and feature extraction[electronic resource] /by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan. - Singapore :Springer Singapore :2015. - xv, 35 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology, Forensic and medical bioinformatics,2191-530X. - SpringerBriefs in applied sciences and technology.Forensic and medical bioinformatics..

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.

ISBN: 9789812873200 (electronic bk.)

Standard No.: 10.1007/978-981-287-320-0doiSubjects--Topical Terms:

182126
Signal processing
--Digital techniques.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

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