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

Nonfiction, Health & Well Being, Medical, Specialties, Orthopedics, Science & Nature, Technology, Engineering
Cover of the book EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan, Springer Singapore
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Author: Bita Mokhlesabadifarahani, Vinit Kumar Gunjan ISBN: 9789812873200
Publisher: Springer Singapore Publication: February 10, 2015
Imprint: Springer Language: English
Author: Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
ISBN: 9789812873200
Publisher: Springer Singapore
Publication: February 10, 2015
Imprint: Springer
Language: English

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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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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