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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.
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