Title: Towards classification of low-level finger movements using forearm muscle activation: a comparative study based on ICA and Fractal theory

Authors: Ganesh R. Naik, Dinesh K. Kumar, Sridhar P. Arjunan

Addresses: School of Electrical and Computer Engineering, RMIT University, G.P.O. Box 2476V, Melbourne, Victoria 3001, Australia. ' School of Electrical and Computer Engineering, RMIT University, G.P.O. Box 2476V, Melbourne, Victoria 3001, Australia. ' School of Electrical and Computer Engineering, RMIT University, G.P.O. Box 2476V, Melbourne, Victoria 3001, Australia

Abstract: There are number of possible rehabilitation applications of surface Electromyogram (sEMG) that are currently unreliable, when the level of muscle contraction is low. This paper has experimentally analysed the features of forearm sEMG based on Independent Component Analysis (ICA) and Fractal Dimension (FD) for identification of low-level finger movements. To reduce inter-experimental variations, the normalised feature values were used as the training and testing vectors to artificial neural network. The identification accuracy using raw sEMG and FD of sEMG was 51% and 58%, respectively. The accuracy increased to 96% when the signals are separated to their independent components using ICA.

Keywords: BSS; blind source separation; ICA; independent component analysis; sEMG; surface electromyogram; EMG; fractal dimension; source separation; low-level muscle activities; finger movements; forearm muscles; rehabilitation; rehab; muscle contraction.

DOI: 10.1504/IJBET.2011.041121

International Journal of Biomedical Engineering and Technology, 2011 Vol.6 No.2, pp.150 - 162

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 09 Jul 2011 *

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