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

• Main Authors: Mokhlesabadifarahani, Bita (Author), Gunjan, Vinit Kumar (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2015.
• Series: SpringerBriefs in Applied Sciences and Technology,
• Subjects: Engineering.; Forensic science.; Health informatics.; Orthopedics.; Rehabilitation.; Bioinformatics.; Biomedical engineering.; Biomedical Engineering.; Forensic Science.; Computational Biology/Bioinformatics.; Health Informatics.
• Physical Description: XV, 35 p. 17 illus., 13 illus. in color. online resource.
• ISBN: 9789812873200
• ISSN: 2191-530X

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.