Design of a Virtual Robotic Arm based on the EMG

variation

Ho-Sun Shin1, Asilbek Ganiev 2

, Kang-Hee Lee2

1Department of Cultural Contents, Soongsil University,

369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea

2Department of Digital Media, Soongsil University,

369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea

Corresponding Author: kanghee.lee@ssu.ac.kr

Abstract. This paper studies design of a virtual robotic arm based on

electromyography (EMG), gyroscope and accelerometer. Data from a Myo

armband which detects various living body signals is delivered through

Bluetooth to a virtual robotic arm which is built in Unity 3D. EMG is electrical

signal generated by tension and relaxation of muscles and a virtual robotic arm

based on EMG is available to control minute movements of forearm muscles

without dynamic motions. Thus, a virtual robotic arm based on EMG is more

suitable to develop applications for hand amputee than a virtual robotic arm

based on gyroscope and accelerometer.

Keywords: Myo armband, Electromyography, Controlling virtual robotic arm,

Unity 3D.

1 Introduction

The purpose of this paper is to design a virtual robotic arm controlled by EMG of

different motions. The reason to basing EMG is to show manipulating virtual robotic

arms in minimum muscle’s movements.

The device which measures EMG, gyroscope and accelerometer, Myo armband

detects electrical activity in forearm muscles [1]. Human forearm has different kinds

of muscles, each of them has different appointment, and these muscles control our

wrist’s movements. And this device connects with other devices or computer via

Bluetooth and it is very comfortable to measure living body signal.

The remaining structure of this paper is outlined as follows: The next section

briefly introduces tools used to make a virtual robotic arm. Section 3 shows

This work was supported by the National Research Foundation of Korea Grant funded by the

Korean Government(NRF-2013S1A5A8020988) And this work was also supported by the

National Research Foundation of Korea Grant funded by the Korean Government(NRF-

2014R1A1A1A05008028)

Advanced Science and Technology Letters Vol.113 (Art, Culture, Game, Graphics, Broadcasting and Digital Contents 2015), pp.38-43

http://dx.doi.org/10.14257/astl.2015.113.09

ISSN: 2287-1233 ASTL Copyright © 2015 SERSC

architecture design and section 4 describes experimental results of virtual robotic

arms. These procedures are also carried out to demonstrate their features.

2 Motivation and Related Research

The proposed system gets data from electromyography, gyroscope and accelerometer

and analyzes them to understand the wrist gesture by measuring which part of

forearms muscles electro activated.

2.1 EMG and Myo sensor

Most gesture-control systems still detect movements with cameras, which can be

thrown off by poor lighting conditions, distance, and simple obstructions. By drawing

gesture information directly from your arm muscles instead of a camera, Myo

circumvents all these problems and also works with devices that don’t have a camera

in the first place [2].

EMG is an electro-diagnostic technique for evaluating and recording the electrical

activity produced by skeletal muscles. Electromyography detects the electrical

potential generated by muscle cells when these cells are electrically or neurologically

activated. The signals can be analyzed to detect medical abnormalities, activation

level, or recruitment order or to analyze the biomechanics of human movement [3].

Fig.1. Myo armband to control devices and games.

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Copyright © 2015 SERSC 39

3 Architecture design

This section shows Myo armband which has eight different blocks and each of them

contains a medical-grade EMG sensor. The armband also has a three-axis gyroscope

and three-axis accelerometer.

Section 3.1 shows the architectural design for controlling a virtual robotic arm.

Fig. 2. Five hand gestures and their functions.

Fig. 3. Myo armband and the number of sensors.

3.1 Design of virtual robotic arm

Figure 4 shows the whole process of controlling a virtual robotic arm in Unity 3D.

Advanced Science and Technology Letters Vol.113 (Art, Culture, Game, Graphics, Broadcasting and Digital Contents 2015)

40 Copyright © 2015 SERSC

Fig. 4. Architecture of the proposed virtual robotic arm.

4 Experimental results

(a) Wave left (b) Fingers spread

Fig. 5. Controlling robotic arm with gestures using EMG sensor.

Figure 5 is an example of controlling virtual robotic arm by EMG sensors. In this

case, five gestures: wave left for turning left robotic arm, wave right for turning right,

fingers spread for raising up, fist puts down and double tap with fingers are for

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Copyright © 2015 SERSC 41

automatically finding balloons and blow them. It is because EMG can detect minute

activities generated tension and relaxation of forearm muscles [4].

(a) Rising up (b) Putting down

Fig. 6. Control of robotic arm by gyroscope and accelerometers data in X, Y, Z directions.

Figure 6 represents the control of virtual robotic arm by using gyroscope and

accelerometer. In this case, robotic arm moves in X, Y and Z directions.

5 Conclusion

Nowadays, the fields of electromyography (EMG) and Electroencephalogram (EEG)

are still actively studied. Also, frequency of practical use for rehabilitation and

nursing becomes high. The study mostly is used for purposes of curing and

rehabilitation. Among living body signals like EEG, the field of EMG which has high

accessibility to measure and analysis is more vigorous than other fields.

This paper showed how to control a virtual robotic arm that prototype was built in

Unity 3D by using electromyography, gyroscope and accelerometer sensors. Because

of EMG sensors, we could get clear and important data and we used them to control a

virtual robotic arm. By comparing virtual robotic arms based on EMG, and gyroscope

and accelerometer, we judged which type of virtual robotic arm is suitable for hand

amputee. Experimental results show the control a virtual robotic arm based on

gyroscope and accelerometer. It should go with dynamic movement of whole arm. On

the other hand, a virtual robotic arm based on EMG needs only specific muscles

activities [5]. Therefore, a virtual robotic arm based on EMG is more helpful to use in

case of hand amputee.

References

1. MYO, http://www.myo.com.

2. Myo reads your muscles for the snappiest gesture tracking ever devised,

http://www.digitaltrends.com/pc-accessory-reviews/

Advanced Science and Technology Letters Vol.113 (Art, Culture, Game, Graphics, Broadcasting and Digital Contents 2015)

42 Copyright © 2015 SERSC

3. Stella Y.Botelho.: Comparison of simultaneously recorded electrical and mechanical

activity in myasthenia gravis patients and in partially curarized normal humans. The

American Journal of Medicine. 19, 693--696 (1995).

4. M.B.I. Raez, M.S. Hussain, F.Mohd-Yasin.: Techniques of EMG signal analysis :

detection, processing, classification and applications, Biological Procedeures Online,

BioMed Central. 8, 11—35 (2006)

5. Mondelli, Mauro, Aretini, Alessandro, Greco, Giuseppe.: Knowledge of

electromyography(EMG) in patients undergoing EMG examinations, Functional

Neurology, CIC Edizioni internationali. 29, 195—200 (2014)

Advanced Science and Technology Letters Vol.113 (Art, Culture, Game, Graphics, Broadcasting and Digital Contents 2015)

Copyright © 2015 SERSC 43