Title :
EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps
Author :
Huang, Han-Pang ; Liu, Yi-Hung ; Liu, Li-Wei ; Wong, Chun-Shin
Author_Institution :
Dept. fo Mech. Eng., Nat. Taiwan Univ., Taipei, Taiwan
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.
Keywords :
autoregressive processes; backpropagation; cascade systems; electromyography; fuzzy neural nets; medical signal processing; prosthetics; self-organising feature maps; signal classification; EMG classification; back-propagation neural network; cascaded architecture; electromyograph; fourth-order autoregressive model; fuzzy algorithm; histogram; k-nearest neighbor method; neural networks; prehensile postures; supervised multilayer feedforward neural network; unsupervised Kohonens self-organizing map; Electromyography; Fuzzy neural networks; Inference algorithms; Linear discriminant analysis; Machine learning algorithms; Multi-layer neural network; Neural networks; Prosthetic hand; Self organizing feature maps; Signal processing algorithms;
Conference_Titel :
Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on
Print_ISBN :
0-7803-7736-2
DOI :
10.1109/ROBOT.2003.1241803