Document Detail


Use of sEMG in identification of low level muscle activities: Features based on ICA and fractal dimension.
MedLine Citation:
PMID:  19963459     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.
Authors:
Ganesh R Naik; Dinesh K Kumar; Sridhar Arjunan
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  1     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2009  
Date Detail:
Created Date:  2009-12-07     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  364-7     Citation Subset:  IM    
Affiliation:
Faculty of Electrical and Computer Engineering, RMIT University Melbourne, GPO BOX 2476 V, Melbourne, Australia - 3001.
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