Practical Surface EMG Pattern Classification byUsing a Selective Desensitization NeuralNetworkHiroshi Kawata1, Fumihide Tanaka1 , Atsuo Suemitsu2 , and Masahiko Morita1
1Graduate School of Systems and Information Engineering, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki, Japan
2
School of Information Science, Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa, Japan
kawata@bcl.esys.tsukuba.ac.jp, fumihide@iit.tsukuba.ac.jp,
sue@jaist.ac.jp, mor@bcl.esys.tsukuba.ac.jp

Abstract. Real-time pattern classification of electromyogram (EMG)
signals is significant and useful for developing prosthetic limbs. However,
the existing approaches are not practical enough because of several limitations in their usage, such as the large amount of data required to train
the classifier. Here, we introduce a method employing a selective desensitization neural network (SDNN) to solve this problem. The proposed
approach can train the EMG classifier to perform various hand movements by using a few data samples, which provides a highly practicalmethod for real-time EMG pattern classification.
Keywords: EMG Pattern Classification, Selective Desensitization Neural Network, Prosthetic Limb, Hand Movement Classification.

1Introduction

Hands play an important role in our lives. The classification of hand movements
by using surface electromyogram (EMG) signals is an important research issue
in the development of prosthetic limbs. Although there is an extensive history ofresearch in this field, the real-time robust implementation of this methodology
is still practically very difficult [1,2]. First, because each hand movement is associated with multiple muscles, the surface EMG signal obtained from a sensor
is the superposition of all the signals obtained from the related muscle activity;
hence, complicating the correspondence relationship between movements andsignals. Second, surface EMG signals are not reproducible, because there is alarge difference between individuals, and even within a person the signals tend
to fluctuate on every trial. As a result, in order for the existing approaches towork, the following conditions have been assumed:– Collect sufficient data samples from the subject.
– Choose the number of sensors carefully in order to avoid redundancy, which
often causes harmful effects while learning the data.
K.W. Wong et al. (Eds.): ICONIP 2010, Part II, LNCS 6444, pp. 42–49, 2010.
c Springer-Verlag Berlin Heidelberg 2010

Practical Surface EMG Pattern Classification

43– Choose the positions of the sensors carefully.
– The subject needs to be trained in advance, so that he/she can deliver stable
EMG signals.
– Preprocess the obtained signals carefully, so that the data-learning algorithm
can produce a satisfactory EMG classifier.
– Extract suitable features from the data samples (for the same reason asabove).
All these requirements made the real-time EMG pattern classification practicallydifficult to implement.
On the other hand, a selective desensitization neural network (SDNN) [3]performs significantly better in approximating a wide range of functions by using
few training data samples. Therefore, in this paper, we will exploit the SDNN
for the classification of the surface EMG pattern. In particular, we will apply
this method to the problem of hand-movement classification, wherein real-time
performance is crucial, particularly for prosthetic limbs.

2

Research Background

2.1

Electromyogram

Muscle contraction is triggered by the excitement of muscle fibers, which is invoked by a signal from the alpha motor neurons in the spinal cord. The electricalpotential difference measured through the muscle contraction is called a myogenic potential, and its time-series signal is called an EMG. Since an EMG occurs
30–100 ms before the muscle contraction, it is considered theoretically possible
to estimate the occurrence of the corresponding bodily movement from the EMG
signals before the actual movement (muscle contraction) occurs.
For measuring the EMG signals, two types of electrodes can be used: needle
electrodes and surface electrodes. The needle electrodes target specific muscle
fibers and measure EMG signals with precision. However, they are accompaniedwith a physical pain to the subject, because the needle has to be inserted into
the subject’s skin. On the other hand, in the case of surface electrodes, there islittle pain, as there is no needle insertion involved to measure the EMG signals.
Instead, the electrical potential measured by the surface electrodes is a summation of the local electrical potentials, which makes the exact estimation of
the corresponding bodily movement more difficult than that in the case of usingneedle electrodes.
In this study, we will use surface electrodes, considering the advantage and totry overcoming the disadvantage described above by introducing the SDNN.
2.2

Selective Desensitization Neural Network

The SDNN [3] is known to have overcome the several limitations of the multilayer
perceptron, and to ably approximate a wide range of functions by using few
training data samples. Here, we will be illustrating an example of approximating

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H. Kawata et al.

a function y = f (x) by employing the SDNN, given a continuous-valued input
vector x = (x1 , . . . , xm ), where m ≥ 2.
The input layer of the SDNN consists of m neuronal groups (G11 , . . . , G1m ).
Each group is composed of n neurons and represents an input variable xµ , i.e.,
the input variable is represented in a distributed manner by the activity patternsof the neurons. Then, the middle layer of the SDNN consists of m(m−1) neuronalgroups G2µ,ν (µ, ν = 1, . . . , m; µ != ν). The neurons in G2µ,ν are connected withboth the neurons in G1µ and G1ν (µ != ν), in the input layer. This realizes aprocedure called desensitization, which neutralizes the output of the neuron
regardless of its input and inner potential. For example, if a neuron is configured
to output either 1 or −1 with equal probabilities as its default output, it willoutput 0 in the case that the neuron is desensitized. Finally, the output layerof the SDNN consists of n′ neurons, each of which is connected with all theneurons in the middle layer. The output of the i-th neuron in the output layer
is calculated by!
nX Xµ,ν µ,ν
ωij xj − hi ,(1)yi = gµ,ν(!=µ) j=1µ,ν
where hi is a threshold, ωij
is a synaptic weight from the j-th neuron of G2µ,ν

in the middle layer, and g(u) is the activation function, where g(u) = 1 for u > 0
and 0 for u ≤ 0.
Learning of this network is performed using a target vector p = (p1 , . . . , pn′ ).
The threshold and the synaptic weights between the middle layer and the output
layer are specifically updated byµ,νµ,ν
← ωi,j+ c(pi − yi )xµ,ν
ωi,jj ,hi ← hi − c(pi − yi ),

(2)
(3)

where c is a learning coefficient.

3Methods

3.1

Signal Measurement

Personal-EMG (Oisaka Electronic Device Ltd. [4]) equipment is used to measure
the surface EMG signals. This can measure the integral of the EMG signal(IEMG) and the original EMG at the same time. In this study, the EMG andIEMG signals are sampled at 3 kHz by using a 12-bit A/D converter, and forthe classification of hand movements, an IEMG signal is used, which is low-passfiltered with a cut-off frequency of 4.8 Hz.
Regarding the myoelectric sensors, we use 10 pairs of wet-electrodes, which arepasted around the subject’s right arm (Fig. 1). The sensors target the following
six muscles: flexor carpi radialis, flexor digitorum profundus, flexor carpi ulnaris,
extensor digitorum, flexor carpi long radialis, brachioradialis, and biceps brachii[5]. However, the sensors do not need to be positioned accurately.

Practical Surface EMG Pattern ClassificationnoneCFig. 1. Placement of wet electrodes

3.2AD45BEFFig. 2. Seven categories to be classified
from the EMG signals [6]Target Hand Movements

In this study, six hand movements (wrist flexion, wrist extension, grasping,
opening up, wrist supination, and wrist pronation) and no-movement conditions are targeted for the classification. In the following sections, we denote theno-movement condition by “basic position (none),” wrist flexion by “movementA,” wrist extension by “movement-B,” grasping by “movement-C,” opening up
by “movement-D,” wrist supination by “movement-E,” and wrist pronation by“movement-F” (Fig. 2).
3.3

Preprocessing of IEMG Signals

Preprocessing is performed to handle the IEMG signals with the SDNN (Fig. 3).
First, each IEMG signal is normalized by the maximum value at each channel,
and the normalized IEMG signals are then normalized again by the maximum
value at each time step. Next, each IEMG channel is connected to a neuronalgroup in the input layer of the SDNN, and each neuronal group is composedof multiple neurons, as described in the previous section. In consequence, wecode the value of the IEMG signal so that only 50% of the neurons can be in acontinual excited state, and the pattern of excitement can depict the continuouschange in the IEMG value consecutively (Fig. 4).
3.4

Learning of the SDNN

The internal structure of the SDNN is shown in Fig. 5. In this study, the input layer of the SDNN is composed of 360 neurons: 300 neurons for 10 IEMG
channels, 30 neurons for the total value of all the IEMG signals, and 30 neurons

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H. Kawata et al.

EMG amplifier

Preprocessing
Learning
SDNN

Classification data-Correct data

Fig. 3. Training process of the proposedsystemlowhigh: non-excited neuron (-1): excited neuron (1)Fig. 4. Distributed coding of IEMG signals

for the difference of the total IEMG value from a step in the past. There are
two middle layers composed of a total of 6600 neurons: in the first layer, halfof the neurons are desensitized by the corresponding neurons in the input layer,
except for the neurons representing the total value of the IEMG signals; andin the second layer, the desensitization procedure is repeated by the neurons
representing the total value of the IEMG signals. The output layer is composedof six neurons, each of which corresponds to the classifier of each movement.
In the learning cycle, we train the SDNN by supplying the preprocessed input
signals greater than the noise threshold and the target patterns representing
the corresponding movement. The synaptic weights from the middle layer tothe output layer and the thresholds in the output layer are specifically modified
according to Eqs. (2) and (3). Here, the training is repeated 10 times and thelearning coefficient c is set to 0.1.
3.5

Evaluation of Classification

In order to evaluate the classification ability of the proposed system after learning, we define a classification rate for each movement as follows. First, a test
data sample is fed into the system and movement detection is performed inevery frame. Second, if any movement has been detected more than six times,
the test data sample is classified into the movement detected most frequently;
otherwise, it is classified into “none”. Third, we judge whether the classificationis correct or not. For example, the classification is regarded as correct if theclassified movement is the same as that corresponding to the test data sample.
Finally, we apply this procedure to all the test data samples corresponding to thesame movement, and then calculate the classification rate as a percentage of thenumber of correct classifications to the total number of the test data samples.

Practical Surface EMG Pattern Classification

47

Channel 1
Movement-A
Channel 2

Movement-B

Channel 10
Movement-F
Difference

Total Value

Fig. 5. Structure of SDNN4

Experiment

To collect the IEMG data, five male subjects are asked to execute one movementfor 2 s and repeat all the six movements (Fig. 2), three times in the same orderin a session. The session is repeated nine times, which provides the total numberof data samples.
After the measurement, a cross validation is performed to calculate the final classification rate: (1) pick up one session data (which contains three data
samples for every movement) to train the SDNN whose classification rates arecalculated by using the other eight session data as test data, (2) repeat it bychanging the training data samples for all combinations, and (3) compute thetotal average as the final classification rate.
Figure 6 shows an example of the IEMG signals obtained from one subject
when the subject performs the six movements. Each line corresponds to thesignal from a channel, and each shaded box represents the movement that islabeled. From this figure, it can be seen that the signals are very unstable andfluctuate at every trial.
Figure 7 plots the final classification rate for each movement of each subject.
The average classification rates over the six movement categories are (s1) 86.73%,(s2) 100.00%, (s3) 92.44%, (s4) 97.15%, and (s5) 100.00%. The total averageclassification rate over the five subjects and the six movements is 95.26%.
Figure 8 shows an example of the total value of the IEMG signals together
with the outputs (classified movements) of six neurons in the output layer of theSDNN. The shaded regions represent the movements classified by the SDNN.

H. Kawata et al.

AMovementDCBFE

ch1 ch2 ch3 ch4 ch5 ch6 ch7 ch8 ch9 ch100.10
0.20

IEMG signal [mV]

0.200.200.100.1
00.500.200.1
00.600510090Classification Rate [%]48807060504030

subject 1 (s1)subject 2 (s2)20subject 3 (s3)subject 4 (s4)10subject 5 (s5)0A

10BTime [s]Fig. 6. Example of IEMG signals from asubjectACDEFMovement

Fig. 7. Final classification rate for movements A–F for five subjectsCBDEFTotal IEMG signal [mV]

10.8
0.6
0.4
0.2
00246Time [s]81012Fig. 8. Example of real-time classification of six movements

Each classification is computed together with an increase in the total IEMG
value, implying that the real-time classification is achieved (see the video at [7]).

5Conclusion

By introducing the SDNN into the pattern classifier, the real-time pattern classification of multiple hand movements was presented. The experimental resultsfrom the five human subjects showed that only three training data samples forPractical Surface EMG Pattern Classification

49

each movement are sufficient for the proposed system to output a classification
accuracy of >95% (average) for the six targeted hand movements. This approachis considered to be more practical than the existing methods for the following
reasons:– It does not require large number of training data samples to obtain a goodclassifier.
– It does not require the user to position sensors on optimal locations.
– It does not require complicated preprocessing of the signal data.
– It does not require the subject to be trained or to be given detailed instructions in advance.
Future work includes more detailed analyses on both the number of training
data samples and sensors. Furthermore, because the SDNN exhibits high performance in approximating a wide range of functions, it is considered to be able not
only to classify the categories of movements but also to estimate the speed/forceof each movement.

Acknowledgments
This study was supported by a Grant-in-Aid for Scientific Research on Priority
Areas (21013007) and a Grant-in-Aid for Young Scientists (B) (21700576) from
the Ministry of Education, Culture, Sports, Science and Technology of Japan(MEXT), and a Grant-in-Aid for Scientific Research (B) (22300079) from theJapan Society for the Promotion of Science (JSPS).

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on Information and Systems J92-D(1), 93–103 (2009)
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http://volga.esys.tsukuba.ac.jp/~ kawata/demovideo/demovideo.wmv