EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction / / by Bita Mokhlesabadifarahani, Vinit Kumar Gunjan

• Record Nr.: UNINA9910299687003321
• Autore: Mokhlesabadifarahani Bita
• Pubbl/distr/stampa: Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015
• ISBN: 981-287-320-1
• Edizione: [1st ed. 2015.]
• Descrizione fisica: 1 online resource (43 p.)
• Collana: SpringerBriefs in Forensic and Medical Bioinformatics, , 2196-8845
• Disciplina: 502.85; 570285; 610.28; 614.1; 616.7; 617.03; 620; 621.3848
• Soggetti: Biomedical engineering; Orthopedics; Forensic science; Bioinformatics; Health informatics; Rehabilitation; Biomedical Engineering and Bioengineering; Forensic Science; Computational Biology/Bioinformatics; Health Informatics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Introduction to EMG Technique and Feature Extraction -- Methodology for working with EMG dataset -- Results -- Conclusions and Inferences of Present Study.
• Sommario/riassunto: 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.