EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction [electronic resource] / by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan.
By: Mokhlesabadifarahani, Bita [author.].
Contributor(s): Gunjan, Vinit Kumar [author.] | SpringerLink (Online service).
Material type:![materialTypeLabel](/opac-tmpl/lib/famfamfam/BK.png)
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.
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