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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...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Colección: | SpringerBriefs in Forensic and Medical Bioinformatics,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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|---|---|---|---|
| 001 | 978-981-287-320-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220425071946.0 | ||
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| 008 | 150210s2015 si | s |||| 0|eng d | ||
| 020 | |a 9789812873200 |9 978-981-287-320-0 | ||
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| 100 | 1 | |a Mokhlesabadifarahani, Bita. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction |h [electronic resource] / |c by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2015. | |
| 300 | |a XV, 35 p. 17 illus., 13 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a SpringerBriefs in Forensic and Medical Bioinformatics, |x 2196-8853 | |
| 505 | 0 | |a Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Biomedical engineering. | |
| 650 | 0 | |a Orthopedics. | |
| 650 | 0 | |a Forensic sciences. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Medical informatics. | |
| 650 | 0 | |a Clinical psychology. | |
| 650 | 0 | |a Rehabilitation. | |
| 650 | 0 | |a Mentally ill-Rehabilitation. | |
| 650 | 1 | 4 | |a Biomedical Engineering and Bioengineering. |
| 650 | 2 | 4 | |a Orthopaedics. |
| 650 | 2 | 4 | |a Forensic Science. |
| 650 | 2 | 4 | |a Computational and Systems Biology. |
| 650 | 2 | 4 | |a Health Informatics. |
| 650 | 2 | 4 | |a Rehabilitation Psychology. |
| 700 | 1 | |a Gunjan, Vinit Kumar. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 776 | 0 | 8 | |i Printed edition: |z 9789812873217 |
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||