EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction (eBook)

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.

Ms. Bita is an Occupational Therapist with dignified academic background over eight years experience in treatment of multiple sclerosis, Neuro-rehabilitation, Orthopedic Rehabilitation and researcher role in the Neuro-rehabilitation research, Ergo Design and treatment field of an esteemed Rehabilitation centre. Presently she works in synergy with medical practitioner of high repute while operating from private practice to contribute to society and medical fraternity.
Mr. Vinit Kumar Gunjan is an Assistant Professor at AITS, Rajampet India. He also serves as the Secretary of IEEE Computer Society of Hyderabad Chapter. He worked with Tata Consultancy Services and SET Noida before joining AITS. Vinit is member of several IEEE Societies, ACM, ACCS, IE and others. He has several National and International Publications to his credit.

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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.