Kontrol Robot Menggunakan Gerakan Mata Berbasis Sinyal Electrooculography (EOG)

  • Kemahyanto Exaudi Program Studi Teknik Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya, Palembang, Indonesia
  • Rendyansyah Rendyansyah Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya, Palembang, Indonesia
  • Aditya Putra Perdana Prasetyo Program Studi Teknik Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya, Palembang, Indonesia
Keywords: bluetooth, elektroda, electrooculography, robot beroda

Abstract

Biomedical technology has now been widely adopted as a means of monitoring the human body in real-time. For example, to detect eye movement. In the medical world, eye movement can be used to determine the type of disease. With the application of human-machine interface (HMI) technology, eyeball movement can be developed in the robotics industry as robot navigation. For example, by moving the eyeball left and right, the robot can interpret the eye signal to move left and right. The interaction between the eyeball movement and the robot is of particular concern in this study. This study aimed to design a measuring instrument for eye movement detection using Electrooculography (EOG) techniques to move a wheeled robot. The EOG measuring instrument consisting of an instrument differential amplifier, a low pass filter, and a high pass filter has been applied in this research. The signal generator technique on EOG is carried out by placing electrodes on three sides of the face, namely forehead (G), left horizontal (H-), right horizontal (H +). The experimental results showed a significant difference between the left and right eye movement amplitude signals. This amplitude is used to classify the movement of the robot wheel towards the left and right. The process of sending robot signals and EOG measuring instruments uses Bluetooth HC-05 serial communication. Based on the research results, it is proven that the robot manages to move left and right according to the eyeball movement.

Downloads

Download data is not yet available.

References

R. Anchan, A. Pillay, A. Kale, A. Bhadricha, and S. P. Ram, “Optimal Bipolar Lead Placement in Electrooculography (EOG): A Comparative Study with an Emphasis on Prolonged Blinks,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1–7.
J. Malmivuo and R. Plonsey, Principles and Applications of Bioelectric and Biomagnetic Fields. New York: Oxford University Press, 1995.
M. R. Kim and G. Yoon, “Control Signal from EOG Analysis and Its Application,” Int. Sch. Sci. Res. Innov., vol. 7, no. 10, pp. 1352–1355, 2013.
Triadi, I. Wijayanto, and S. Hadiyoso, “Mouse Menggunakan Sinyal Electrooculogram Menggunakan Metode Continuous Wavelet Transform,” e-proceeding Eng., vol. 5, no. 3, pp. 5279–5284, 2018.
A. B. Kanwade, R. V Gone, S. J. Ahire, and A. R. Borkar, “Study of EOG signal generation , Analyses , and acquisition system,” Int. Res. J. Eng. Technol., vol. 04, no. 04, pp. 3378–3382, 2017.
W. S. M. Sanjaya, D. Anggraeni, R. Multajam, M. N. Subkhi, and I. Muttaqien, “Design and Experiment of Electrooculogram ( EOG ) System and Its Application to Control Mobile Robot,” IOP Conf. Ser. Mater. Sci. Eng, vol. 180, no. 012072, 2017.
J. Ma, Y. Zhang, A. Cichocki, and F. Matsuno, “A Novel EOG / EEG Hybrid Human-Machine Interface Adopting Eye Movements and ERPs : Application to Robot Control,” vol. 9294, no. c, pp. 1–14, 2014.
J. D. Creel, “The Electroretinogram and the Electro-oculogram: Clinical Applications,” 2012. [Online]. Available: http://webvision.umh.es/Webvision/ClinicalERG.html.
L. Y. Deng, C. Hsu, T. Lin, J. Tuan, and S. Chang, “Expert Systems with Applications EOG-based Human – Computer Interface system development,” Expert Syst. Appl., vol. 37, no. 4, pp. 3337–3343, 2010.
A. R. Chaidir, G. A. Rahardi, K. Anam, G. A. Rahardi, and K. Anam, “Navigasi robot bergerak berdasarkan landmark garis menggunakan kontroler Braitenberg dan pengolahan citra Mobile robot navigation based on line landmarks using the Braitenberg controller,” J. Teknol. dan Sist. Komput., vol. 8, no. July, pp. 185–191, 2020.
A. Amirkhani, M. Shirzadeh, M. H. Shojaeefard, and A. Abraham, “Controlling wheeled mobile robot considering the effects of uncertainty with neuro-fuzzy cognitive map,” ISA Trans., vol. 100, pp. 454–468, 2020.
A. Wibisono, “Desain Robot Beroda dengan Sistem Kendali Headset Gelombang Otak,” Joined J. (Journal Informatics Educ. Vol 3 No 2 Vol. 3 Nomor 2 (2020)DO - 10.31331/joined.v3i2.1234 , Dec. 2020.
P. S. Shrungare, A. A. Bokde, N. U. Kashte, and S. S. Raut, “Smart Phone Based Robot for Domestic purpose using Bluetooth .,” Int. Res. J. Eng. Technol., vol. 5, no. 1, pp. 694–697, 2018.
M. S. Masmoudi, N. Krichen, M. Masmoudi, and N. Derbel, “Fuzzy Logic Controllers Design For Omnidirectionnal Mobile Robot Navigation,” Appl. Soft Comput. J., 2016.
K. Exaudi, A. P. P. Prasetyo, and others, “Navigasi Berbasis Behavior dan Fuzzy Logic pada Simulasi Robot Bergerak Otonom,” J. Nas. Tek. ELEKTRO, vol. 5, no. 1, 2016.
S. Aparigraha, W. Kurniawan, and A. S. Budi, “Implementasi Metode Complementary Filter pada Pengendali Robot Mobil menggunakan Gestur Tangan Manusia,” J. Peng. Tek. Inf. Ilm. Kom. vol. 3, no. 10, pp. 9788–9797, 2019.
J. Karki, “Active low-pass filter design,” Texas Instruments Appl. Rep., 2000.
W. Wijaya, F. Syahroni, C. D. Mulyadi, W. Sani, A. Lukman, and H. P. Nurba, “Two Axis Simple CNC Machines Based on Microcontroller and Motor Driver Shield IC L293D,” in 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA, 2020, pp. 1–5).
Published
2021-09-10
Section
Articles
Abstract viewed = 154 times
PDF downloaded = 137 times