|
|
|
|
| LEADER |
02924nam a22005655i 4500 |
| 001 |
978-981-287-320-0 |
| 003 |
DE-He213 |
| 005 |
20151111211341.0 |
| 007 |
cr nn 008mamaa |
| 008 |
150210s2015 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
|
| 040 |
|
|
|d GrThAP
|
| 050 |
|
4 |
|a R856-857
|
| 072 |
|
7 |
|a MQW
|2 bicssc
|
| 072 |
|
7 |
|a TEC009000
|2 bisacsh
|
| 082 |
0 |
4 |
|a 610.28
|2 23
|
| 100 |
1 |
|
|a Mokhlesabadifarahani, Bita.
|e author.
|
| 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.
|
| 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 Applied Sciences and Technology,
|x 2191-530X
|
| 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 Engineering.
|
| 650 |
|
0 |
|a Forensic science.
|
| 650 |
|
0 |
|a Health informatics.
|
| 650 |
|
0 |
|a Orthopedics.
|
| 650 |
|
0 |
|a Rehabilitation.
|
| 650 |
|
0 |
|a Bioinformatics.
|
| 650 |
|
0 |
|a Biomedical engineering.
|
| 650 |
1 |
4 |
|a Engineering.
|
| 650 |
2 |
4 |
|a Biomedical Engineering.
|
| 650 |
2 |
4 |
|a Orthopedics.
|
| 650 |
2 |
4 |
|a Forensic Science.
|
| 650 |
2 |
4 |
|a Computational Biology/Bioinformatics.
|
| 650 |
2 |
4 |
|a Health Informatics.
|
| 650 |
2 |
4 |
|a Rehabilitation.
|
| 700 |
1 |
|
|a Gunjan, Vinit Kumar.
|e author.
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer eBooks
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9789812873194
|
| 830 |
|
0 |
|a SpringerBriefs in Applied Sciences and Technology,
|x 2191-530X
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-981-287-320-0
|z Full Text via HEAL-Link
|
| 912 |
|
|
|a ZDB-2-ENG
|
| 950 |
|
|
|a Engineering (Springer-11647)
|
