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One of the sample data shows as fig.2, it contains of six lead of EMG signals. After that EMG is disposed by Labview software and the motion pattern of prosthetic hand will be classified. We adopt 2500 points in the original source signal so it can provide enough data when using ICA to decompose it. The decomposing process of SEMG signal is that we decompose SEMG signal to each motor unit which constitutes motor electric potential component. Each motor unit can be regarded as independent component. If we suppose each source signal is statistic independently, and the process of creating SEMG can be regarded linearly. The Informax ICA algorithm is applied to decompose SEMG, after the experiment, we get the result in fig.3, which shows that the source signals have been separated and the basic motor unit action potentials are obtained. Input Vector A Weight Updating layer, the number of implicit layer units, the request of input, the study of velocity and the deliver of function, etc, are analyzed. With the ameliorations, the capability of the network is improved. The BP neural network is trained to be able to simulate the pattern recognition procedure. We adopt the AR coefficients of different pattern as input of the BP neural network. Input layer have three nodes which names Err or Figure 4. Three layer BP Neural Network Y Output Vector Y as Y 1 , Y 2 , Y3 which corresponding to pattern Laxation, hand opening, hand closing respectively. To avoid the influence of gain in the procedure of signal processing, we normalize the AR coefficients in input layer. Figure 3. EMG disposed by Informax algorithm After many experiments, we find motor unit shrinked by MUAPT s4 changing obviously when extensor carpi ulnaris transfer its motion pattern, so s4 s will be used for the following analyzing procedure, 1 - s 3 , s5 - s6 will be discarded as noise. Then we extract 4-order AR coefficient in Labview. Tanbe.1 shows typical 4-order AR coefficient derived from AR model of motions of extensor carpi ulnaris. The acquired average values of the 4-order AR coefficients, as the characteristic vector of the surface electromyography signals, are used in the recognition of motion mode. TABLE I THE 4-ORDER AR COEFFICIENTS OF ELECTROMYOGRAPHY SIGNALS OF EXTENSOR CARPI ULNARIS AR Coefficients Pattern A01 A02 A03 A04 Laxation -0.28 -0.05 0.02 -0.03 Hand opening -0.59 -0.19 0.53 -0.16 Hand closing -0.83 0.07 0.29 -0.21 IV. CONCLUTION This paper introduced Informax ICA to analyze SEMG signal. Under the premise that each source signal is independent, the experiment shows that it can eliminate unmeasurabled noise jamming, and separate each source signal independently. At the same time, the experiment shows that the effect of Informax is well. Then we analyze surface electromyography signals and extract 4-order AR coefficient from AR model as characteristic vectors. The electromyography signals of pattern Laxation, hand opening, hand closing could be recognized by BPNN. The proposed method achieves performance in terms of categorization, recognition ratio and robustness. The experiment proved that it is viable and effective to extract from surface electromyography signals based on the AR mode and use BPNN as a categorizing tool to recognize movement in the application of recognizing wrist movement. But the result also shows that the indeterminacy in amplitude and permutation has some trouble in signal disposal. Recently, a new blind signal separation method which names natural gradient ICA is presented to separate medical signals. It is said that the indeterminacy in amplitude and permutation can be eliminated by using this method. So the author’s next plan is to confirm the validity of this new method by simulation experiment in the area of SEMG. In this paper, a three layer BP Neural Networks was applied to construct two models of ANN with Sigmoid function. The issue of modifying a typical BP neural network in a practical way to meet EMG pattern recognition is discussed. Then muscle’s pattern recognition procedure is given by Neural Network in MATLAB which shows as fig.4. The structure of input REFERENCES [1] A. Hyvärinen and E. Oja. Independent Component Analysis: Algorithms and Applications[J]. Neural Networks, 2000, 13(4-5):411-430.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73. 244
[2] Kumaravel, N., and Kavitha, V., Automatic diagnosis of neuromuscular disease using neural network. Biomed. Sci. Instrum. 90:245–250, 1994 [3] Butchal, F., An Introduction to EMG, Copenhagen, Gyldendal, 1957. [4] Frize, M., Ennett, M., Stevenson, M., and Trigg, C. E., Clinical decision support for intensive care unit using ANN. Med. Eng. Phys. 23:217–222, 52001. [5] Chen Liangzhou, Shi Wenkang. Time Sequence Analysis Random Sets Method[J].College Journal of Shanghai Communication University 2005,39(3),400-404. [6] Liu Jiahai. Evaluation Research on Muscular Function Status based on SEMG Signal Non-linear Kinetic Analysis [M].Thesis of Doctor Degree Zhejiang University,2001. [7] A.Hyvarinen, A Family of Fixed-point Algorithms for Independent Component Analysis[J]. In(ICASSP'97), IEEE Int. Conf. on Acoustics, Speech and Signal Processing Munich, Germany, 1997, 45(8) , 3917-3920. [8] A.Hyvarinen, Fast and Component Analysis[J]. IEEE Robust Fixed-point Algorithms Traps. On Neural Networks, for Independent 1999, 25(10), 626-634. [9] J.-F. Cardoso(1999). Higher order contrast for independent component analysis. Neural Computation. 11(1):157-193. [10] V. Soon, et al. (1990). An extended fourth order blind identification algorithm in spatially correlated noise. Proc. IEEE ICASSP:1365-1367. 245
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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ISBN 978-952-5726-09-1 (Print) Proc
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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