Use of sEMG in identification of low level muscle activities: features based on ICA and fractal dimension.
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.
Naik
Ganesh R
GR
Faculty of Electrical and Computer Engineering, RMIT University Melbourne, GPO BOX 2476 V, Melbourne, Australia - 3001. ganesh.naik@rmit.edu.au
Kumar
Dinesh K
DK
Arjunan
Sridhar
S
D000328:Adult; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005260:Female; D005385:Fingers / Q000502:physiology; D017709:Fractals; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D005082:Physical Exertion / Q000502:physiology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult