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024 7 $a10.1007/978-981-287-320-0
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035   $a(DE-He213)978-981-287-320-0
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035   $a(PPN)184495342
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100   $a20150210d2015      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aEMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction /$fby Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
205   $a1st ed. 2015.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2015.
215   $a1 online resource (43 p.)
225 1 $aSpringerBriefs in Forensic and Medical Bioinformatics,$x2196-8845
300   $aDescription based upon print version of record.
311   $a981-287-319-8 
320   $aIncludes bibliographical references.
327   $aIntroduction to EMG Technique and Feature Extraction -- Methodology for  working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.
330   $aNeuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.
410  0$aSpringerBriefs in Forensic and Medical Bioinformatics,$x2196-8845
606   $aBiomedical engineering
606   $aOrthopedics
606   $aForensic sciences
606   $aBioinformatics
606   $aMedical informatics
606   $aRehabilitation
606   $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
606   $aOrthopedics$3https://scigraph.springernature.com/ontologies/product-market-codes/H45000
606   $aForensic Science$3https://scigraph.springernature.com/ontologies/product-market-codes/B23000
606   $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050
606   $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060
606   $aRehabilitation$3https://scigraph.springernature.com/ontologies/product-market-codes/H55006
615  0$aBiomedical engineering.
615  0$aOrthopedics.
615  0$aForensic sciences.
615  0$aBioinformatics.
615  0$aMedical informatics.
615  0$aRehabilitation.
615 14$aBiomedical Engineering and Bioengineering.
615 24$aOrthopedics.
615 24$aForensic Science.
615 24$aComputational Biology/Bioinformatics.
615 24$aHealth Informatics.
615 24$aRehabilitation.
676   $a502.85
676   $a570285
676   $a610.28
676   $a614.1
676   $a616.7
676   $a617.03
676   $a620
676   $a621.3848
700   $aMokhlesabadifarahani$b Bita$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721003
702   $aGunjan$b Vinit Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299687003321
996   $aEMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction$92507763
997   $aUNINA