TY  - BOOK
AU  - Mokhlesabadifarahani,Bita
AU  - Gunjan,Vinit Kumar
ED  - SpringerLink (Online service)
TI  - EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction
T2  - SpringerBriefs in Applied Sciences and Technology,
SN  - 9789812873200
AV  - R856-857 
U1  - 610.28 23
PY  - 2015///
CY  - Singapore
PB  - Springer Singapore, Imprint: Springer
KW  - Engineering
KW  - Forensic science
KW  - Health informatics
KW  - Orthopedics
KW  - Rehabilitation
KW  - Bioinformatics
KW  - Biomedical engineering
KW  - Biomedical Engineering
KW  - Forensic Science
KW  - Computational Biology/Bioinformatics
KW  - Health Informatics
KW  - Electronic books
KW  - local
N1  - Introduction to EMG Technique and Feature Extraction -- Methodology for  working with EMG dataset -- Results -- Conclusions and Inferences of Present Study
N2  - 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
UR  - http://ezproxy.alfaisal.edu/login?url=http://dx.doi.org/10.1007/978-981-287-320-0
ER  -