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

@inproceedings{Huang2003EMGCF,
  title={EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps},
  author={Han-Pang Huang and Yi-Hung Liu and Li-Wei Liu and Chun-Shin Wong},
  booktitle={ICRA},
  year={2003}
}
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… CONTINUE READING
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