EMG Controlled Artificial Hand and Arm Design

MUSTAFA KUTAY ULUBAS

Department of Electrical and Electronics Engineering

Suleyman Demirel University

Faculty of Engineering

TURKEY

AHMET AKPINAR

Department of Electrical and Electronics Engineering

Suleyman Demirel University

Faculty of Engineering

TURKEY

OZLEM COSKUN

Department of Electrical and Electronics Engineering

Suleyman Demirel University

Faculty of Engineering

TURKEY

MESUD KAHRIMAN

Department of Electrical and Electronics Engineering

Suleyman Demirel University

Faculty of Engineering

TURKEY

Abstract: - Today, there are many people who have lost their hands or arms for various reasons. This situation affects

both psychology and daily life of people negatively. With the developing technology, prosthetic hand and arm studies

are carried out to facilitate the life of disabled people and to eliminate this negativity. Thanks to the existing biopotentials

in the body, it is possible to read the human body. In this context, it can explain our hand and arm movements with the

existing biopotential signals and transfer these signals to a prosthesis, enabling people to make the desired movement.

Since the biopotential signals in the body are of very low amplitude and frequency, the first goal is to obtain the EMG

signal cleanly without noise. In this study, the obtained analog signal was converted into digital information by using

software in the computer environment. Thus, each signal gained a meaning. As a result, the movement of the prosthesis

was provided by transferring it to stepper motors with the help of Arduino.

Key-Words: EMG, Artificial hand and arm, MATLAB

Received: April 12, 2021. Revised: January 22, 2022. Accepted: February 21, 2022. Published: March 26, 2022.

WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

DOI: 10.37394/23208.2022.19.6

Mustafa Kutay Ulubas, Ahmet Akpinar,

Ozlem Coskun, Mesud Kahriman

E-ISSN: 2224-2902

Volume 19, 2022

1 Introduction

With the development of technology, people have tried to

produce robots that can replicate the movement they want

to do. Today, robot movements are provided by using

mostly EMG signals. After the Second World War, the

first engineering studies were started with the

encouragement of the US government to develop artificial

limbs.

While the prostheses were controlled with simple cable-

driven methods at the first stage, later on, prostheses that

are suitable for human physiology and that can be

automatically controlled by the electrical activity created

by the muscles have begun to be made [1].

In recent years, many studies on anthropomorphic hand

design have been reached. The hand prostheses that are

emerging day by day are more similar to the functions of

the human hand [2]. Commercial and easy-to-reach

prostheses are generally simple in construction.

These simple structures are an important factor affecting

the functionality of the hand, as in the Ottobock hand [3].

In addition to the disadvantages of commercial prostheses,

prostheses known as FZK hand [4], RTRII hand [5], SDM

hand [6], SmartHand [7], Vanderbilt University hand [8],

which are much more functional as a result of academic

studies, are also at the forefront today. The first EMG-

controlled prostheses use the on-off method or simple

proportional control methods.On the other hand, the

working speed of prostheses is constant and slow. This

makes it difficult for amputees to use prostheses as their

own hands [9].

Since 1981, the Utah Arm Prosthesis has been the most

widely used EMG-controlled arm prosthesis for people

with amputated elbows [10]. First, Dr. It was developed

by the University of Utah Engineering Design Center, led

by Steve Jacobsen. In 1987, Motion Control, which made

the Utah Arm Prosthesis the world's most durable and

reliable EMG-controlled arm prosthesis available,

launched the Utah-2 Arm Prosthesis with a completely

redesigned electronic design [11].

In 2004, Motion Control Utah-3 introduced

microprocessor technology to the Arm Prosthesis, which

enables the prosthodontist or user to make necessary

adjustments to achieve maximum performance. A wide

variety of inputs are available, so more users have more

options. Meanwhile, the U-3 still offers the same precise,

proportional elbow, hand and wrist control that allows the

user to move the arm and hand slowly or quickly in any

position.

2 EMG Signal

Electromyogram is defined as the electrical activity that

occurs on muscles during resting and contraction states.

Electromyography (EMG) is the recording and

interpretation of action potentials in muscles [12]. The

EMG signal is a complex, non-stationary and noisy signal.

The amplitude of the EMG signal is random and can

usually be expressed as a Gaussian distribution. The

amplitude range of the signal is 0-10 mV (peak to peak)

or 0-1.5 mV (rms). While the usable energy of the EMG

signal is in the frequency range of 0-500 Hz, the dominant

energy is in the range of 50-150 Hz [13].

In medical applications, the characteristics of the signals

produced by the muscles are used in the diagnosis of

disorders in muscle or neural functions [14]. In this way,

it is possible to attribute electrical meanings to people's

movements. EMG signals that are loaded with meaning

can be transferred to the prosthetic arm and can perform

the movements of the arm depending on the human desire.

In the light of this information, by using EMG signals

from the body, the movements of the prosthetic hand or

arm can be provided without any external influence. A

classic EMG signal is given in Figure 1 below.

Figure 1. EMG signal

Needle and surface electrodes are used to record EMG

signals. If a nerve damage is to be examined or a single

muscle fiber is desired to be reached, needle EMG is

preferred, while surface EMG is used for researches on

electrical activity in a very large area or for examining the

surface muscles.

3 Methods of Measuring EMG Signals

EMG measurement setups seen in clinics generally consist

of electrodes for detecting EMG signals, stimulating,

amplifier, oscilloscope, magnetic recorder and loudspeaker.

In addition to these, some signal processing blocks,

spectrum analyzers and computers can be found for

research targeted studies.

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DOI: 10.37394/23208.2022.19.6

Mustafa Kutay Ulubas, Ahmet Akpinar,

Ozlem Coskun, Mesud Kahriman

E-ISSN: 2224-2902

Volume 19, 2022

By stimulating the motor nerve of the muscle to be

examined with the stimulant, the EMG signals in the

muscle fibers are transferred to the amplifier and from there

to the relevant imaging unit with the help of receiving

electrodes. EMG schemes are performed as a single

compact unit for ease of application and transport, and

sometimes for general purposes, capable of measuring

biopotentials other than muscle signals [15].

Figure 2. EMG scheme block diagram

The block diagram of the system used to obtain EMG

signals is given in Figure 2 [15]. Many electrode

shapes have been designed to collect biomedical

signals. Bioelectrodes are tools for converting ionic

conductivity to electronic conductivity and making it

workable in electronic circuits. The general purpose

of bioelectrodes is to collect medically important

bioelectrical signals such as electrocardiogram,

electroencephalogram, electromyogram. These

electrodes are surface electrodes and internal

electrodes [16].

4 Material and Method

The designed system consists of three main parts: (1)

EMG signal processing circuit, (2) transferring the

obtained analog signal to the computer environment

and (3) transferring the digital signals to the servo

motor.

4.1. EMG Signal Processing Circuit

Two signal processing circuits are implemented to provide the

movement of the hand and wrist separately. To collect EMG

signals, electrodes must be attached to the biceps muscles,

which consist of skeletal fiber bundles. Three electrodes are

required for each movement. Two of them were connected to

the biceps muscles and applied to the input of the measurement

biopotential.

The third one is connected to a different point on the arm and

used as a reference point. Since EMG signals are too small to

be processed by the microcontroller, they must be amplified

before being fed to the microcontroller. A band-pass filter is

used to increase the signal-to-noise ratio and suppress other

physiological signals such as ECG. In the study; The band-pass

filter is designed from high- and low-pass filters with cut-off

frequencies of 50 and 500 Hz.

4.2. Transferring Obtained Analog Signal to the

Computer

MATLAB and ARDUINO processors were used as

software in the graphical and numerical acquisition of the

EMG signal received from our body in the computer

environment. The voltage supply, analog output and

ground terminals of the EMG sensor circuit are connected

to the ARDUINO processor as shown in Figure 3.

Figure 3. Connection of equipment with each other

With these connections, the graphical output of the

signal is obtained in the MATLAB environment as

seen in Figure 4.

WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

DOI: 10.37394/23208.2022.19.6

Mustafa Kutay Ulubas, Ahmet Akpinar,

Ozlem Coskun, Mesud Kahriman

E-ISSN: 2224-2902

Volume 19, 2022

Figure 4. Example of the resulting graph

4.3. Transferring Digital Signals to Servo Motor

The movement of the motor was determined with the

numerical data determined according to the opening and

closing of the hand, accompanied by the numerical data

obtained. In the light of these numerical data, the motor

has been turned to 180 degrees when the hand is closed

and 0 degrees when it is open. The numerical data

obtained according to the opening and closing moment of

the hand are given in Figure 5.

Figure 5. Numerical data obtained according to the

opening and closing moment of the hand

5 Results

According to the movements of the hand and arm, the

graphical outputs of the EMG signals were obtained with

the help of MATLAB and their changes were observed.

Graphics such as the opening and closing of the hand, the

uncontracted state of the muscle, the left and right

movements of the arm are given in Figure 6, Figure 7,

Figure 8 and Figure 9.

Figure 6. Muscle without contraction

Figure 7. The moment of closing and opening of the

hand

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DOI: 10.37394/23208.2022.19.6

Mustafa Kutay Ulubas, Ahmet Akpinar,

Ozlem Coskun, Mesud Kahriman

E-ISSN: 2224-2902

Volume 19, 2022

Figure 8. Graph showing arm movement to the left

Figure 9. Graph showing arm movement to the right

6 Conclusions and Discussion

In recent years, the development and use of prosthetic

devices has greatly changed and facilitated the lives

of people with disabilities. The increasing number of

disabled people and the fact that every healthy

individual is a disabled candidate has increased the

importance of prosthetic devices.

In this study, by obtaining EMG signals and using

these signals, servo motors are driven, thus opening

and closing of a hand is realized. The EMG signal

best shows the muscle condition of the person

receiving the signal.

For this reason, it is very suitable as a source signal

for the removable prosthesis in the use of prosthesis.

On the other hand, the fact that the system is portable

and its size is small within the possibilities has created an

advantage for the user.

In addition to the difficulties that people who lost

their limbs face in daily life, they also experience

psychological disorders. The developed prostheses

help to keep the difficulties experienced at a low

level. Today, modern robotic prostheses, which are at

the product level as well as being prototypes, are

promising as new technologies that need to be

studied. Unfortunately, EMG-based prosthesis studies

are very limited in our country. It is hoped that these

high-tech prostheses will reach the targeted points

with the collaboration of different disciplines.

References:

[1] Andrade NA, Borges GA, Nascimento FA de O,

Romariz AR, Rocha AF. A new biomechanical hand

prosthesis controlled by surface electromyographic

signals. Conf Proc IEEE Eng Med Biol Soc. 2007.

6142-5, 2007.

[2] Kaplanoglu E. Design of shape memory alloy

based and tendon-driven actuated fingers towards a

hybrid anthropomorphic prosthetic hand. International

Journal of Advanced Robotic Systems. Vol. 9, 77,

2012.

[3] Otto Bock myoelectric arm prostheses.

http://www.ottobock.com/cps/rde/xchg/ob_com_en/hs

.xsl /384.html, 2013.

[4] Pylatiuk C, Mounier S, Kargov A, Schulz S,

Bretthauer G. Progress in the development of a

multifunctional hand prosthesis. 26th IEEE Annual

Int. Conf. on Engineering in Medicine and Biology

Society. San Francisco, USA, pp. 4260–4263, 2004.

[5] Zollo L, Roccella S, Tucci R, Siciliano B.

Biomechatronic design and control of an

anthropomorphic artificial hand for prosthetics hand

robotic applications. Biorob2006, Pisa. no. 4, pp.

418–429, 2007.

[6] Dolar AM and Howe RD. The SDM hand as a

prosthetic terminal device: a feasibility study. in Proc.

2007 IEEE Int. Conf. On Rehabilitation Robotics. The

Netherlands, pp. 978–983, 2007.

WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

DOI: 10.37394/23208.2022.19.6

Mustafa Kutay Ulubas, Ahmet Akpinar,

Ozlem Coskun, Mesud Kahriman

E-ISSN: 2224-2902

Volume 19, 2022

[7]Cipriani C, Controzzi M and Carrozza MC.

Objectives, criteria and methods for the design of the

smart hand transradial prosthesis. Robotica. vol. 28,

no. 6, pp. 919-927, 2010.

[8] Dalley SA, Wiste TE, Varol HA, Goldfarb M. A

multigrasp hand prosthesis for transradial amputees.

In Proc. of Intl. Conf. of the Engineering in Medicine

and Biology Society, EMBC,Argentina. Aug. 31 –

Sept. 4, 2010.

[9] Okuno R, Fujikawa M, Yoshida M, Akazawa K.

Biomimetic hand prosthesis with easily

programmable microprocessor and high torque motor.

Engineering in Medicine and Biology Society, 2003.

Proceedings of the 25th Annual International

Conference of the IEEE. vol.2, no., pp.1674,1677

Vol.2, 17-21, 2003.

[10] Jacobsen SC, Knutti DF, Johnson RT, and Sears

HH. Development of the Utah artificial arm. IEEE

Trans. Biomed. Eng. vol. BME- 29, no. 4, pp. 249–

269, April 1982.

[11] Sears HH, Andrew JT, and Jacobsen SC.

Experience with the Utah arm hand and terminal

device. In Comprehensive Management of the Upper-

Limb Amputee, D.J. Atkins and R.H. Meier III, Eds.

New York: Springer-Verlag, 1989.

[12] Bronzino JD. The biomedical engineering

handbook. Second edition, CRC PresLLC, Boca

Raton: FL, 2000.

[13] DeLuca CJ. Surface Electromyography

detecetion and recording. Delsys, 2002.

[14] Günay M, Alkan A. Clustering EMG signals

using the K-means algorithm. KSU Journal of

Engineering Sciences. 12 (2), 25-30, 2009.

[15] Yazgan E, Korurek M. Medical electronics,

Istanbul Technical University. Faculty of Electrical

and Electronics, 1996.

[16] Hyatt R, Dayton J, Dayton D. The measurement,

instrumentation and sensors handbook. New York:

Crc Press, 1999.

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WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

DOI: 10.37394/23208.2022.19.6

Mustafa Kutay Ulubas, Ahmet Akpinar,

Ozlem Coskun, Mesud Kahriman

E-ISSN: 2224-2902

Volume 19, 2022