DocumentCode :
2779735
Title :
Hybrid EMG Recognition System by MDA and PCA
Author :
Matsumura, Yuji ; Fukumi, Minoru ; Mitsukura, Yasue
Author_Institution :
Tokushima Univ., Tokushima
fYear :
0
fDate :
0-0 0
Firstpage :
5294
Lastpage :
5300
Abstract :
In this paper, we propose a recognition system of wrist operation by focusing on ElectroMyoGram (EMG), that is, the living body signal generated with movement of a subject. In previous research, we only performed pattern recognition by Neural Network (NN) and Fast Fourier Transform (FFT). In contrast, in proposal research, we try to improve recognition accuracy and reduce learning-time of system by combining Multi Discriminant Analysis (MDA) and gradual Principal Component Analysis (PCA) based on the PCA result of EMG data. From results of computer simulation, it is shown that our approach is effective for improvement in recognition accuracy and speed.
Keywords :
electromyography; fast Fourier transforms; medical signal processing; neural nets; pattern recognition; principal component analysis; Electromyogram; MDA; PCA; computer simulation; fast Fourier transform; hybrid EMG recognition system; multidiscriminant analysis; neural network; principal component analysis; Agricultural engineering; Electromyography; Fast Fourier transforms; Neural networks; Optical fiber devices; Pattern recognition; Position measurement; Principal component analysis; Signal generators; Wrist;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.247285
Filename :
1716836
Link To Document :
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