EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction
Mokhlesabadifarahani, Bita.
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan. - 1 recurso en línea (XV, 35 páginas 17 ilustraciones, 13 ilustraciones a color.) - SpringerBriefs in Forensic and Medical Bioinformatics, 2196-8845 Engineering (Springer-11647) .
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.
9789812873200
10.1007/978-981-287-320-0 doi
Procesadores digitales de señal
TK5102.9 / 2015 EB
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan. - 1 recurso en línea (XV, 35 páginas 17 ilustraciones, 13 ilustraciones a color.) - SpringerBriefs in Forensic and Medical Bioinformatics, 2196-8845 Engineering (Springer-11647) .
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.
9789812873200
10.1007/978-981-287-320-0 doi
Procesadores digitales de señal
TK5102.9 / 2015 EB