SURFACE EMG CLASSIFICATION FOR PROSTHESIS

CONTROL

Fuzzy Logic vs. Artificial Neural Network

Siti Anom Ahmad

, Mohd Asyraf Khalid

, Asnor J. Ishak

and Sawal H. M. Ali

Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia

Serdang, Selangor, Malaysia

Department of Electrical, Electronics and System, Faculty of Engineering and Built Environment

Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

Keywords: Prosthesis control, Electromyography, Classification, Fuzzy logic, Artificial neural network.

Abstract: Electromyography control system (ECS) is a well-known technique for prosthesis control application. It

consists of two main modules namely feature extraction and classification. This paper presents the

investigation of the classification module in the ECS. The surface electromyographic (EMG) signals were

recorded from flexor and extensor muscles of the forearm during wrist flexion and extension. Standard

deviation and mean absolute value were used to extract information from the raw EMG signals. Two

different classifiers, fuzzy logic and artificial neural network were used in investigating the surface EMG

signals. The classifier is responsible to determine the movement of the subject’s limb during specific

moment. The two classifiers were compared in terms of their performance.

1 INTRODUCTION

Prosthetic hand has been designed to provide

replacement to people with hand or complete arm

amputation. In the USA, there are approximately

40,000 people registered with hand amputation and

this number is increasing every year. In the last 30

years, the amputees had been provided with either

passive or active prosthetic hands to help them in

their daily lives. However, a survey shows that there

are 30% to 50% of the handicapped did not use the

prosthetic hands regularly (Hardeep and Arora

2010). Among the reasons for the rejection are

heavy weight, limited functionality and stiff/robot

like movement.

Continuous studies and researches have been

carried out in improving the prosthetic hand design

with the main aim to have a hand that can best

mimic a normal human hand. To improve the

functionality of the hand, two main factors have to

be considered in the development process. These

two factors are the structural design and the control

mechanism of the hand. Rapid growth in the

structural design of the prosthetic hand can be seen

and there is renewed interest in the development of

hands with multiple degrees of freedom that lead to

multiple grip hand postures (Mitchell, 2008). The

control mechanism has become the main concern in

the prosthetic hand development process. Various

methods have been proposed in controlling the

operation of a prosthetic hand and surface

electromyography has become the preferred

technique for the control mechanism of the

prosthesis control application (Hudgins, 1999;

Ajiboye, 2005) The concept of using surface

electromyography signal for prosthesis control

started in the 1940s (Plettenburg, 2006). By using

the residual muscles on the amputee's arm, they can

be used as the control channel to determine the final

movement of the hand. The simplest application is to

either open or close hand.

Electromyography (EMG) signal is a technique

that is used to describe electrical current produced

by skeletal muscles during contractions. In general,

EMG can be categorized into two: needle and

surface EMG. The later type is the most commonly

used in many applications as it is totally non-

invasive and low cost. Surface EMG (SEMG) finds

application in many areas that include rehabilitation

of disabled (Huang, 1999), prosthetics (Nagata,

2004); (Chappell, 2009)

and human computer

317

Anom Ahmad S., Asyraf Khalid M., J. Ishak A. and H. M. Ali S..

SURFACE EMG CLASSIFICATION FOR PROSTHESIS CONTROL - Fuzzy Logic vs. Artiﬁcial Neural Network.

DOI: 10.5220/0003696603170320

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 317-320

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

interface (Fukuda, 2004). The commonality in these

systems is the need to classify SEMG to identify the

control commands. These examples and many others

(Huang, 1999); (Tsuji, 2000) used multichannel

EMG for the purpose of discrimination between

classes (Kumar, 2008).

EMG has been used to control the movement of

a prosthetic hand and known as EMG control system

(ECS). Two main processes involve in the prosthesis

control system are feature extraction and

classification. Feature extraction process is where

the raw SEMG signal is represented into a feature

vector which is then used to separate the desired

output, e.g. different hand grip postures. Various

feature extraction techniques have been reported in

this prosthesis control field. Mean absolute value

(MAV) has been the most widely used method to

extract information from a SEMG signal (Hudgins,

1999); (Chan, 2000). The information obtained in

the feature extraction will then be fed to a classifier.

A classifier is responsible in mapping different

pattern and matches them appropriately to determine

the final output. Artificial neural network (ANN) is

one of the classification methods and has been used

in most of the EMG classification systems reported

in the literature (Hudgins, 1999); (Ajiboye, 2005).

Another method that has been used for classification

is fuzzy logic (FL) (Chan, 2000); (Weir, 2003).

The objective of this work is to compare two

classification techniques; ANN and FL in finding the

final grip posture of a prosthetic hand. The research

will focus on evaluating the performance between

these two classifiers in terms of accuracy in

classifying the SEMG data. Other aspects of

performance are also discussed.

2 METHODOLOGY

The EMG dataset used to test the methods was

obtained from the University of Southampton, in UK

(Chappell, 2009). The EMG signals were recorded

from five participants’ forearm muscles, namely

flexor carpi ulnaris (FCU) and extensor carpi radialis

(ECR) with a reference electrode at the elbow. The

participants were asked to do wrist flexion and

extension. The signals were recorded using Noraxon

Ag/AgCl dual electrodes (diameter 15 mm, centre

spacing 20mm). The procedures for surface

electrodes placement were referred from SENIAM

(Hermans, 1999). Prior to the electrode placement,

the electrodes sites were prepared by cleansing the

skin surface with rubbing alcohol to reduce the

impedance at the surface.

The recorded EMG signals were post-processed

for further analysis. Two methods were used in the

feature extraction stage are standard deviation (SD)

and mean absolute value (MAV).

To preserve the information in the EMG signals,

the whole data was divided into overlapping

segments. Each segment consists of 200 data points

and the current segment overlaps with the previous

segment by 50 points. A moving data window was

applied to the data sequence and the SD and MAV

within the data were calculated repeatedly.

The extracted features were then fed into two

types of classifier: ANN and FL. The output from

the classifiers will be different postures of hand

grips. However, in this work the grip postures are

represented in the ‘STATE’ form. Based on two

inputs, SD and MAV, both classifiers will give one

final output which is STATE1, STATE2 or

STATE3.

For the FL classifier design, a Mamdani type

fuzzy logic was used in. The rules were created

based on states of contraction and the design process

ran over a few cycles of analysis until the most

optimum classification system is achieved and it was

done manually. The shapes for the inputs in the

membership function were the S-type and the output

was in triangular shape only. The defuzzification

will be set to centroid. Two FL classification

systems have been developed, which are with and

without solver. Solver is an additional tool in

Simulink to smooth the graph by building a time of

the next simulation step and applies a numerical

method to solve the set of ordinary differential

equations that represent the model. Figure 1 shows

the Simulink model for the FL classification system

without solver.

Figure 1: Simulink model for the FL classification system.

Initially, the project was to use only two inputs

from SD and MAV. But during the rule building, it

is found out that it is difficult to differentiate

between State 1 and State 3 for the same amplitude.

Thus to solve the problem, another set of input was

added. The new input is the difference of current

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

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value of SD with the previous value of SD. To

simplify the calculation, the sign (positive or

negative) was omitted because it also exhibits same

results.

For the ANN classifier design shown in Figure 2,

two layers feed forward ANN with two hidden

layers were used. Feed forward method was chosen

instead of feedback due to its simplicity and easily

understood algorithm. The number of neurons used

was 20 in order to make sure that ANN does not

consume too much computational resources. For the

validation of the network, two datasets have been

created; training and test datasets.

Figure 2: ANN classification system block diagram.

3 RESULTS AND DISCUSSION

Figure 3 show the EMG signals during wrist

extension recorded from ECR respectively. The

muscles contractions can be seen clearly as there are

distinguishable low voltage period between them.

Figure 3: EMG signals during wrist extension from ECR.

The results (SD and MAV) obtained in the

feature extraction stage were then used in the rules

and membership functions development in fuzzy

classifier and algorithm development in ANN.

The classification result for FL and ANN are

shown in Figure 4 and Figure 5 respectively. For

ANN classifier, the classification results were

presented using confusion matrix diagram and one

of the results is shown in Figure 5. For the FL

classification, the results are based on with and

without solver.

The performance of the classifiers was

determined by calculating the percentage of

accuracy. The accuracy was obtained by calculating

the length of the points that not in its desired

position. The total misaligned are divided by the

total of time are and multiplied with 100%. The

ideal graph for each segment is assumed to be linear

thus the points that fall off of its threshold are

considered misclassified (inaccurate).

(a)

(b)

Figure 4: FL classification results (a) without solver and

(b) with solver.

Figure 5: ANN classification result from FCU.

Table I shows the accuracy for both classifiers.

In term of accuracy, the fuzzy classifier obviously is

the best choice by exhibiting more than 90%

0 2 4 6 8 10 12 14

-150

-100

-50

100

150

200

250

Time/ s

uV

SURFACE EMG CLASSIFICATION FOR PROSTHESIS CONTROL - Fuzzy Logic vs. Artificial Neural Network

319

accuracy while the ANN classifier only exhibit

around 30% to 60% accuracy. This is due to the fact

that the fuzzy classifier has rules that can easily

distinguish between states. As for ANN classifier, it

needs to build the algorithm based on the training

data. This means that if the training data is lacking in

some aspects, then the accuracy of study data will

also be affected. In this project, the study data size is

small thus producing vulgar and incomplete patterns.

This affects the accuracy of the result due to the

difficulty of the algorithm to recognize the pattern

from the data. This can be solved by introducing

larger training data set.

Table 1: Classification accuracy.

ANN

without Solver with solver

FCU 83.0% 97.8% 62.0%

ECR 82.9% 97.1% 37.1%

In term of automation, ease of use and capability

to adapt to various samples, the ANN classifier is a

better choice than fuzzy classifier. This is due to the

fact that the user only needs to introduce the input

and the target and the ANN will automatically create

an algorithm and network to recognize the study

data. On top of that, an accurate network that was

produce by ANN can be used on various samples

due to its learning capability. As for fuzzy classifier,

the user need to determine by them the membership

function, rules and need to calculate the accuracy

manually thus consuming a lot of time. However, it

is not a drawback to FL as automatic tuning is

possible (G. Panoutsos, 2010). The main advantage

of FL is the data doesn’t need to be trained like

ANN.

4 CONCLUSIONS

The study is to compare two classification methods

namely FL and ANN to determine the final output

from the extracted SEMG signals of the forearm.

The signals were recorded from FCU and ECR

during wrist flexion and extension respectively.

From the classification accuracy, it shows that

FL gave higher accuracy (>80%) compared to ANN

classifier (60%). This is due to small data size that

leads to the difficulty of the ANN model

development to recognize the pattern. The

performance can be improved by introducing larger

training data set.

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