Mokhlesabadifarahani, Bita. <br/><br/>
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction  [electronic resource] / 
by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan. 
 - XV, 35 p. 17 illus., 13 illus. in color.  online resource. 
 - SpringerBriefs in Applied Sciences and Technology,  2191-530X .
 - SpringerBriefs in Applied Sciences and Technology, .
<br/><br/>Introduction to EMG Technique and Feature Extraction -- Methodology for  working with EMG dataset -- Results -- Conclusions and Inferences of Present Study. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9789812873200 
<br/><br/><label>Standard No.: </label>10.1007/978-981-287-320-0  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Engineering.<br/>Forensic science.<br/>Health informatics.<br/>Orthopedics.<br/>Rehabilitation.<br/>Bioinformatics.<br/>Biomedical engineering.<br/>Engineering.<br/>Biomedical Engineering.<br/>Orthopedics.<br/>Forensic Science.<br/>Computational Biology/Bioinformatics.<br/>Health Informatics.<br/>Rehabilitation.
<br/><br/><label>LC Class. No.: </label>R856-857 
<br/><br/><label>Dewey Class. No.: </label>610.28