EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction
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...
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2015
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oai:scholar.dlu.edu.vn:DLU123456789-578982023-11-11T05:54:44Z EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction Mokhlesabadifarahani, Bita Gunjan, Vinit Kumar Mechanical Engineering Technology Fuzzy systems Digital techniques Signal processing 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. 2015-09-01T03:38:35Z 2015-09-01T03:38:35Z 2015 Book 978-981-287-320-0 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57898 en application/pdf Springer |
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Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Mechanical Engineering Technology Fuzzy systems Digital techniques Signal processing |
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Mechanical Engineering Technology Fuzzy systems Digital techniques Signal processing Mokhlesabadifarahani, Bita Gunjan, Vinit Kumar EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |
| description |
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. |
| format |
Book |
| author |
Mokhlesabadifarahani, Bita Gunjan, Vinit Kumar |
| author_facet |
Mokhlesabadifarahani, Bita Gunjan, Vinit Kumar |
| author_sort |
Mokhlesabadifarahani, Bita |
| title |
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |
| title_short |
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |
| title_full |
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |
| title_fullStr |
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |
| title_full_unstemmed |
EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |
| title_sort |
emg signals characterization in three states of contraction by fuzzy network and feature extraction |
| publisher |
Springer |
| publishDate |
2015 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57898 |
| _version_ |
1819783569370775552 |