國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction/ by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan.
Author:	Mokhlesabadifarahani, Bita.
other author:	Gunjan, Vinit Kumar.
Description:	XV, 35 p. 17 illus., 13 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Biomedical engineering. -
Online resource:	https://doi.org/10.1007/978-981-287-320-0
ISBN:	9789812873200

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. - 1st ed. 2015. - XV, 35 p. 17 illus., 13 illus. in color.online resource. - SpringerBriefs in Forensic and Medical Bioinformatics,2196-8845. - SpringerBriefs in 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

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

588770
Biomedical engineering.

LC Class. No.: R856-857

Dewey Class. No.: 610.28

EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction
LDR:02418nam a22004215i 4500 001 969153
003 DE-He213
005 20200702052029.0
007 cr nn 008mamaa
008 201211s2015 si | s |||| 0|eng d
020 $a 9789812873200 $9 978-981-287-320-0
024 7 $a 10.1007/978-981-287-320-0 $2 doi
035 $a 978-981-287-320-0
050 4 $a R856-857
050 4 $a HC79.E5
050 4 $a GE220
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.28 $2 23
100 1 $a Mokhlesabadifarahani, Bita. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1066546
245 1 0 $a EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction $h [electronic resource] / $c by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan.
250 $a 1st ed. 2015.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2015.
300 $a XV, 35 p. 17 illus., 13 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Forensic and Medical Bioinformatics, $x 2196-8845
505 0 $a Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.
520 $a 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.
650 0 $a Biomedical engineering. $3 588770
650 0 $a Orthopedics. $3 596623
650 0 $a Forensic science. $3 1253722
650 0 $a Bioinformatics. $3 583857
650 0 $a Health informatics. $3 1064466
650 0 $a Rehabilitation. $3 673666
650 1 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 2 4 $a Forensic Science. $3 683486
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Health Informatics. $3 593963
700 1 $a Gunjan, Vinit Kumar. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1064228
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789812873217
776 0 8 $i Printed edition: $z 9789812873194
830 0 $a SpringerBriefs in Forensic and Medical Bioinformatics, $x 2196-8845 $3 1264703
856 4 0 $u https://doi.org/10.1007/978-981-287-320-0
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)