Mokhlesabadifarahani, Bita.
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, .
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
Engineering.
Forensic science.
Health informatics.
Orthopedics.
Rehabilitation.
Bioinformatics.
Biomedical engineering.
Engineering.
Biomedical Engineering.
Orthopedics.
Forensic Science.
Computational Biology/Bioinformatics.
Health Informatics.
Rehabilitation.
R856-857
610.28
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, .
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
Engineering.
Forensic science.
Health informatics.
Orthopedics.
Rehabilitation.
Bioinformatics.
Biomedical engineering.
Engineering.
Biomedical Engineering.
Orthopedics.
Forensic Science.
Computational Biology/Bioinformatics.
Health Informatics.
Rehabilitation.
R856-857
610.28