EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps
Resource
Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on
Journal
IEEE International Conference on Robotics and Automation, 2003
Pages
-
Date Issued
2003-09
Date
2003-09
Author(s)
DOI
1050-4729
Abstract
Electromyograph (EMG) features have the properties of large variations and nonstationary issue in the classification of EMG is the classifier design. The major goal of this paper is to develop a classifier for the classification of eight kinds of prehensile postures to achieve high classification rate and reduce the online learning time. The cascaded architecture of neural networks with feature map (CANFM) is proposed to achieve the goal. The CANFM is composed of two kinds of neural networks: an unsupervised Kohonen's self-organizing map (SOM), and a supervised multi-layer feedforward neural network. Experimental results show that by extracting EMG features, forth-order autoregressive model (ARM) and histogram of EMG signals (IEMG), as inputs, the proposed CANFM can obtain and remain high classification rates compared with other classifiers, including k-nearest neighbor method (K-NN), fuzzy K-NN algorithm, and back-propagation neural network (BPNN) in several online testing.
Type
journal article
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