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
| 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.
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.
