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