DocumentCode :
2373971
Title :
Use of sEMG in identification of low level muscle activities: Features based on ICA and fractal dimension
Author :
Naik, Ganesh R. ; Kumar, Dinesh K. ; Arjunan, Sridhar
Author_Institution :
Fac. of Electr. & Comput. Eng., RMIT Univ. Melbourne, Melbourne, VIC, Australia
fYear :
2009
fDate :
3-6 Sept. 2009
Firstpage :
364
Lastpage :
367
Abstract :
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.
Keywords :
biology computing; electromyography; fractals; independent component analysis; neural nets; pattern recognition; signal detection; signal processing; ICA; artificial neural network; fractal dimension; independent component analysis; low level forearm muscle activity; low level muscle activity identification; sEMG; surface electromyography; Adult; Algorithms; Electromyography; Female; Fingers; Fractals; Humans; Isometric Contraction; Male; Muscle, Skeletal; Pattern Recognition, Automated; Physical Exertion; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Young Adult;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1557-170X
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2009.5332489
Filename :
5332489
Link To Document :
بازگشت