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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: materialTypeLabelBookSeries: SpringerBriefs in Applied Sciences and Technology: Publisher: Singapore : Springer Singapore : Imprint: Springer, 2015Description: XV, 35 p. 17 illus., 13 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789812873200.Subject(s): Engineering | Forensic science | Health informatics | Orthopedics | Rehabilitation | Bioinformatics | Biomedical engineering | Engineering | Biomedical Engineering | Orthopedics | Forensic Science | Computational Biology/Bioinformatics | Health Informatics | RehabilitationAdditional physical formats: Printed edition:: No titleDDC classification: 610.28 Online resources: Click here to access online
Contents:
Introduction to EMG Technique and Feature Extraction -- Methodology for  working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.
In: Springer eBooksSummary: 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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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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