A study on multi-motion pattern recognition of EMG based on genetic algorithm �

ZHANG Qingju SHI KaiDepartment of Information Engineering Department of Automatic Measurement and

Control Shandong University of science and technology Transportation school in shandong province

Taian, Shandong Province, China Taian, Shandong Province, ChinaZhangqj2003@163.com Shik99@163.com

� This work is supported by shandong university of science and technology “chunlei plan” (2010AZZ120)

Abstract— The traditional neural network has the uncertain shortcomings on structures and the genetic algorithm has the optimization ability. So the improved adaptive genetic algorithm is used to optimize the numbers and value of hidden nodes of neural network. And then LMS algorithm is used to optimize the weight and threshold value of the neural network.The nueral network is trainned by much groups oftraining samples and the ultimate neural network system is got.Afterwards, using the improved power spectrum K value method to extract the characteristic value of the collected surface emg signals;Finally,the characteristic values of sixkinds of hand motions is input into RBF neural network classifier for pattern recognition. This trial gets good resultsand 6 kinds of action recognition rate to 75%.

Keywords-Surface Electromyography; Signal; genetic algorithm; RBF neural network; pattern recognition

I INTRODUCTION Electromyography (EMG) signal is a kind of

complicated electric activity of the muscle under the surface skin, and it is the integration of bioelectric signal on time and space. Usually, the Surface Electromyography iscollected on the surface skin of the extensor and flexor using two special electrodes when the arm was bending and stretching. It is used to control the artificial limb as the safe and non-invasive controlling signal. The reference [1] had said that the EMG signal can be used in human movement and biological machinery research. Recently, with the development of scientific technique and the improvement of research measure, the studying of SEMG has got a great achievement and the use of SEMG became more and more widely.

The artificial neural network method is a new technology in recent decades. It has inherent nonlinear characteristics and self-study, organization, adaptive ability and so on. The learning method of Radial Basis Function Neural Network (RBFNN) is simple, convenient and targeted. The reference [2] had said that the RBFNN provided a new idea for the forward network because of its simple structure, fast training process and good nonlinear adaptive signal processing. It has been applied in various fields successfully.

Genetic Algorithm (the abbreviation GA) is a kind of search and optimization algorithms by simulating biologic Genetic and long-term evolution process. It has the

reinforcement learning ability on group optimization, parallelism, rapidity and adaptability. The training of neural network shows a new look because of the appearance of the genetic algorithm. The objective function does not require continuous and derivable but only except calculation. It's search throughout the whole solution space and it is very easy to get the global optimal solution. So many scholars will to combine genetic algorithm and the artificial neural network together and use the genetic algorithm to optimize the topology structure and weights parameters of the neural network. In the paper, the GA is used to optimize the RBF network for pattern recognition and got good classification effect.

II GA OPTIMIZATION RBF NEURAL NETWORKThe characteristic parameters of the RBF network

including hidden nodes, center position and standardization parameters will be optimized by using the GA’s strong parallel information processing ability. The best network structure will be found when it meets specified target function. The basic elements of the genetic algorithm are set as described below.

A. ENCODING The encoding uses real number coding scheme and

code string consists of three components. The encoding is shown just like table 1.

TABLE 1 Coding compositionhidden nodes

numberhidden nodes

centerwide

degreeH C1,C2,…,CH �

In the Table 1, H is the hidden nodes numbers,],1[ tnH � . nt is the number of training samples. ]),1[( HiCi � is any integer between tn~1 and means the

serial number of the training samples. Due to the hidden nodes center has the same dimensions

as the input vector and usually some training sample is considered as the center of hidden layer. So if x means the training sample and pCi � , it’s means the input of the p

groups of training samples are the hidden nodes center, namely pi xc � . The different hidden nodes center is not the same, so HCCC ,,, 21 � is a group of numbers who are two

2012 Second International Conference on Instrumentation & Measurement, Computer, Communication and Control

978-0-7695-4935-4/12 $26.00 © 2012 IEEE

DOI 10.1109/IMCCC.2012.46

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2012 Second International Conference on Instrumentation & Measurement, Computer, Communication and Control

978-0-7695-4935-4/12 $26.00 © 2012 IEEE

DOI 10.1109/IMCCC.2012.46

168

different from each other's. � is the hidden nodes width of the radial basis function,

namely standardization parameters, making each hidden nodes width equal. According to the experiences

Hdm�� .

Where dm is the maximum distance of all the hidden nodes centers H is the number of clustering centers, that is, the number of hidden nodes.

B. INITIALIZATIONIn the MATLAB environment, the network initial group

),(0 LNpop is produced randomly. And each individual :),(0 npop means an independent RBF network, ],1[ Nn� .

The length of the code strings tnL �� 2 , population size N = 20 50.

C. FITNESS FUNCTIONThe code string :),(0 npop is decoded and gets a RBF

network structure. Then the training set of samples is input and calculates the connection weights w between hidden layer and the output layer by LMS or RLS algorithm. And then basing on the value of w, the output of network is calculated. The objective function J can be calculated by formula (1).

))))(((1log()(1 1� �� �

����tn

i

TH

jjiijt

tt cxwy

nnnJ � (1)

Where tn is number of the training samples; it xy , is the expected output value and input value of the training samples in the i group; H is the hidden nodes number,

],1[ tnH � ; w is the connection weights between the hidden layer and output layer; )(� is Gaussian function of the hidden layer; � is the output layer threshold.

Fitness function )(nf can be got by formula (2). The minimization problem of the objective function will be switched into the maximum problem of fitness function.

bnJanf �� )()( (2) Where a is the negative proportion factor; b is the

constant threshold.

D. GENETIC OPERATIONGenetic operation imitates the biological genetic

operation. From the view of the optimization search, genetic operation can make the solution of the problem optimization generation after generation and approximate optimal solution. Genetic operation includes three basic genetic operators: selection, crossover and mutation.

(1) SELECTION In order to avoid the loss of the gene selection operation

is effective and improves the global convergence and computational efficiency thus. In order to ensure individual diversity and improve the computation efficiency of the algorithm, the optimal individual preservation method and roulette method is combined in the paper.

First, the individuals who have the largest and second

big fitness in the father generation individuals will directly go into the crossover operation. And then other individuals are chosen by the roulette method, until producing Nindividuals. The method can reserve the optimal and suboptimal individuals and the individuals whose fitness are low have the chance to be selected too to ensure the diversity of individuals.

The choice probability of population individuals such asthe formula (3) shows.

sum

iN

ii

ii f

f

f

fp ��

��1

(3)

Where if is the fitness of individual i; sumf is the total fitness of the population; ip is the choice probability of the individual i.

The realization of roulette method such as the followingshows.

First, the total fitness of the current population sumf will be calculated.

And then, the random number )(�rand between 0 and 1 will be produced and calculate sumfrands �� )( .

Finally, the m-th individual will be chosen and go into the crossover operation.

The above operation is done 2�N times and gets 2�Nindividuals. And then the individuals who have the largest and second big fitness in the father generation individuals will be combined with the 2�N individuals and go into the crossover operation together.

(2) CROSSOVER Crossover operator is used to produce new individual

and reduce the failure probability of the effective mode at the same time. Crossover probability cp controls the usage frequency of the crossover operation. The larger crossover probability can enhance the genetic algorithm's ability inopening new search area, but the damage likelihood in high performance mode is increased. The reference [5] said if the crossover probability is too low, the search may be in a torpid state. In order to avoid the premature convergence, the reference [6-7] used the adaptive crossover probability. The cp will adjust adaptively with the adaptive values. The adjustment algorithm such as the formula (4) shows.

�

��

�

����

ffk

ffffffkp

c

cc

2

maxmax1 )/()( (4)

Where cf is the larger adaptive value between theparents of father generation; ff ,max is the largest adaptive value and average adaptive value; 21,kk is the constant between 0 and 1.

Because the real number coding scheme is used here, so the crossover is the arithmetic crossover, such as formula (5).

�

��

���

���

12'2

21'1

)1(

)1(

xxxxxx

��

�� (5)

169169

Where 21, xx is the individual of father generation;'2

'1 , xx is the individual of sun generation; � is the real

number between 0 and 1. (3) MUTATIONMutation is the secondary operator in the genetic

operation. It aims to maintain the diversity of groups and make the genetic algorithms have both global and local search capabilities. When the genetic algorithm is near optimal solution’s adjacent domain by means of crossover operator, it can accelerate the convergence to the optimal solution using the mutation operator. Generally the low-frequency mutation can avoid the most important and single gene missing. The high-frequency mutation makes genetic algorithm tend to random search. So the mutation probability mp adjusts adaptively, such as the formula (6).

�

��

�

����

ffk

ffffffkp

m

mmm

5

maxmax4 )/()((6)

Where mf is the fitness of mutation individuals; ff ,max is the largest adaptive value and average adaptive

value; 54 ,kk is the constant between 0and 1. In the mutation operation, mutation position is produced

randomly. In the traditional binary coding scheme, the individual genes turn from 0 to 1 or from 1 to 0. Because of using the real number coding scheme here, the perturbation is subjoined on original individual. That is to add a random number on the original individual. The scheme such as the formula (7) shows.

��� xx ' (7) Where x is the farther generation individual; 'x is the

sun generation individual; � is the random number between 0 and 1.

E. TERMINATION CONDITIONThe genetic generation number is allocated to equal T.

When the maximum generation number achieves T, genetic operation is terminated otherwise to continue.

III EXPERIMENT RESULT AND ANALYSIS The purpose of the experiment is to study and verify the

recognition ability of the pattern classifier that is structured above. The test objects are health volunteers. The test muscle group is ulnar flexor muscle of wrist and extensor in the forearm. The identification wrist actions is wrist turn, wrist next turn, internal rotation, extorsion, fist and expansion fist.

The test process is as the following. First of all the surface EMG signals were collected with two channels at the same time on the ulnar flexor and extensor muscle of wrist. And then the signals were processed through the high and low pass filtering and 50HZ working band resistance filtering. And then the eigenvalue K of different motions was extracted basing on the improved power spectrum eigenvalue extraction method. Finally the MATLAB software was used to simulate the optimized RBF neural

network by the genetic algorithm for pattern recognition.The neural network classifier for single mode had been

designed such as the following Fig.2. The dimensions of the input vector were 2-dimensional. They were the power spectrum k-value of ulnar extensor muscle and ulnar flexor muscle of wrist respectively. The best numbers of the hidden layer nodes was optimized by the genetic algorithm. The maximum generations of genetic algorithm was 100; the network’s output node was 1 and the expected output value was 1 too. When the expected output value was 1, the recognition results were right. When the expected output value was 0, the recognition results were error. Algorithm implementation was simulated by MATLAB platform with programming. The actual output is often not strictly equal to the expected output. In actual testing, when the actual output value was greater than or equal to 0.5, the recognition result was considered to be right. When the actual value was less than 0.5, the recognition result was considered to be error. The training samples of single action were 40 groups and the test samples were 20 groups.

The neural network classifier for 6 modes had 6 output nodes, corresponding to 6 sport modes. They were wrist turn y=[100000], wrist next turn y=[010000], internal rotation y=[001000], extortion y=[000100], fist y=[000010] and exhibition boxing y=[000001]. In the experiment, the training samples were 120 sets of data and each mode corresponded to 20 sets of data. The 60 sets of data were used to test the performance of neural network classifier and each mode corresponded to 10 groups of data.

Table 2 correct recognition rate of each model

action turn next turn

internal rotation extortion fist exhibition

boxingrecognition

rate 100% 90% 95% 100% 70% 60%

Table 3 correct recognition rate of 10 experiments for 6 models experiment

number 1 2 3 4 5

Recognition rate 75% 73.3% 75% 75% 75%

experiment number 6 7 8 9 10

Recognition rate 73.3% 75% 75% 75% 75%

Flexor characteristic value

Figure2. Single mode RBF neural network structure

Extensor characteristic value

Adjustable weightFixed weight

1

1

K sum nuit

2 D

imen

sion

inpu

t vec

tor

jiw y output

Input layer Hidden layer Output layer

� �xRj

170170

The data in the table2 shows the correct recognition rate of 4 kinds of action (wrist turn, wrist next turn, internal rotation and extortion,) is more than or equal to 90%. The attempt that the genetic algorithm optimized the neural network in the paper is feasible. From the table3, the recognition rate of 6 models is 75% approximately. Because of the low recognition rate of fist and exhibition boxing, so the total recognition rate is slightly lower. But the experiment result is satisfactory compared to the traditional methods. In order to enhance reproducibility and universality of neural network, training data and testing data were all derived from the healthy male and female subjects. The characteristic values of 6 kinds of mode were extracted from different subjects and then were input to the network classifier. The experimental results showed that the neural network classification appliance had good universality and fault tolerance and could identify multi-mode of different individuals according to the different trainers.

Figure 3. Electric artificial limb

IV. CONCLUSION AND PROSPECT The try that genetic algorithm and neural network

combination is used in the myoelectricity artificial limbs gets a strong proof. It is encouraging in multi-DOF myoelectricity prosthesis research that the recognition rate of 6 kinds of action can reach 75%. And the neural network has its own adaptive self learning ability. The genetic algorithm can adjust the network structure adaptively for different subjects and the neural network can make response to sample training of different individuals. But in allusion to fist and expansion fist actions, the recognition rate is lower because of the obscure distinction between muscle activities. How to improve the recognition rate of these two kinds of actions will be the key to improve the overall recognition rate.

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[5] Kazuo Kiguchi, etc (2004). Neuro-Fuzzy Control of a Robotic

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