EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps

Han Pang Huang*, Yi Hung Liu, Li Wei Liu, Chun Shin Wong

*此作品的通信作者

研究成果: Conference article同行評審

52 引文 斯高帕斯(Scopus)

摘要

Electromyograph (EMG) features have the properties of large variations and nonstationarity. An important 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 higher 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.

原文English
頁(從 - 到)1497-1502
頁數6
期刊Proceedings - IEEE International Conference on Robotics and Automation
1
出版狀態Published - 2003
事件2003 IEEE International Conference on Robotics and Automation - Taipei, Taiwan
持續時間: 14 9月 200319 9月 2003

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