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

被引：7

作者：

Naik, Ganesh R. ^{[1
]}

Kumar, Dinesh K. ^{[1
]}

Arjunan, Sridhar P. ^{[1
]}

机构：

[1] RMIT Univ, Sch Elect & Comp Engn, GPO Box 2476V, Melbourne, Vic, Australia

来源：

INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY | 2011年 / 6卷 / 02期

关键词：

BSS; blind source separation; ICA; independent component analysis; sEMG; surface electromyogram; FD; fractal dimension; source separation; low-level muscle activities;

D O I：

10.1504/IJBET.2011.041121

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：150 / 162

页数：13