Design of a Virtual Robotic Arm based on the EMG variation

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1 , pp Design of a Virtual Robotic Arm based on the EMG variation Ho-Sun Shin 1, Asilbek Ganiev 2, Kang-Hee Lee 2 1 Department of Cultural Contents, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea 2 Department 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-2013S1A5A ) And this work was also supported by the National Research Foundation of Korea Grant funded by the Korean Government(NRF- 2014R1A1A1A ) ISSN: 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 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. Copyright 2015 SERSC 39

1 shows the architectural design for controlling a virtual robotic arm. Fig. 2. Five hand gestures and their functions. Fig. 3.

3 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. 40 Copyright 2015 SERSC

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

Controlling robotic arm with gestures using EMG sensor.

In this case, five gestures: wave left for turning left robotic arm, wave

4 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 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.

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.

5 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, 2. Myo reads your muscles for the snappiest gesture tracking ever devised, 42 Copyright 2015 SERSC

6 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, (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, (2006) 5. Mondelli, Mauro, Aretini, Alessandro, Greco, Giuseppe.: Knowledge of electromyography(emg) in patients undergoing EMG examinations, Functional Neurology, CIC Edizioni internationali. 29, (2014) Copyright 2015 SERSC 43

Gesture Control in a Virtual Environment. Presenter: Zishuo Cheng (u ) Supervisors: Prof. Tom Gedeon and Mr. Martin Henschke

Gesture Control in a Virtual Environment Presenter: Zishuo Cheng (u4815763) Supervisors: Prof. Tom Gedeon and Mr. Martin Henschke 2 Outline Background Motivation Methodology Result & Discussion Conclusion