Move Your Wheelchair with Your Eyes

Year 2017, Special Issue (2017), 5 - 8, 24.09.2017

Gökçen Çetinel Sevda Gül Zafer Tiryaki Enes Kuzu Meltem Milligüney

https://doi.org/10.18100/ijamec.2017SpecialIssue30462

Cited By: 2

Abstract

In the proposed study, our goal is to move paralyzed people with their eyes.Otherwise, use this document as an instruction set. Paper titles should be written in uppercase and lowercase letters, not all uppercase. For this purpose, we use their Electrooculogram (EOG) signals obtained from EOG goggles completely designed by the authors. Through designed EOG goggles, vertical-horizontal eye movements and voluntary blink detection are verified by using 5 Ag-AgCl electrodes located around the eyes.

EOG signals utilized to control wheelchair motion by applying signal processing techniques. The main steps of signal processing phase are pre-processing, maximum-minimum value detection and classification, respectively. At first, pre-processing step is used to amplify and smooth EOG signals. In maximum-minimum value detection we obtain maximum and minimum voltage levels of the eye movements.  Furthermore, we determine the peak time of blink to distinguish voluntary blinks from involuntary blinks.  Finally, at classification step k-Nearest Neighbouring (k-NN) technique is applied to separate eye movement signals from each other.

Several computer simulations are performed to show the effectiveness of the proposed EOG based wheelchair control system. According to the results, proposed system can communicate paralyzed people with their wheelchair and by this way they will be able to move by their selves.

Keywords

Electrooculogram (EOG), k-Nearest Neighboring (k-NN), wheelchair control

References

• A. B. Usakli and S. Gurkan, “Design of a Novel Efficient Human; Computer Interface: An Electrooculagram Based Virtual Keyboard,” Instrum. Meas. IEEE Trans., vol. 59, no. 8, pp. 2099–2108, 2010.
• M. S. Hossain, K. Huda, and M. Ahmad, “Command the computer with your eye - An electrooculography based approach,” Ski. 2014 - 8th Int. Conf. Software, Knowledge, Inf. Manag. Appl., 2014.
• M. Y. Esas and F. Latifo, “Bilgisayar Tabanlı İki Kanallı Elektrookülografi Cihazı Tasarımı ve Göz Hareketlerinin Takibi Computer-Based Design with Dual Channel Device Electrooculography and Eye Movement Tracking.”, 2015.
• A. López, F. J. Ferrero, M. Valledor, and O. Postolache, “EOG Signal Processing Module for Medical Assistive Systems,” 2016.
• A. Ubeda, E. Ianez, and J. M. Azorín, “An integrated electrooculography and desktop input bimodal interface to support robotic arm control,” IEEE Trans. Human-Machine Syst., vol. 43, no. 3, pp. 338–342, 2013.
• A. Marjaninejad, Sabalan Daneshvar, “A low-cost Real-time Wheelchair Navigation System Using Electrooculography,” no. Icee, pp. 1961–1965, 2014.
• B. Champaty, J. Jose, K. Pal, and A. Thirugnanam, “Development of EOG Based Human Machine Interface control System for Motorized Wheelchair,” 2014.
• R. Barea, L. Boquete, M. Mazo, and E. Lopez, “Wheelchair guidance ‘strategies using EOG,” J. Intell. Robot. Syst., vol. 34, no. 3, pp. 279–299, 200.
• Analog Device- Low Cost Low Power Instrumentation Amplifier, AD620 Data Sheet.Analog Device Inc. AD620 Datasheet [Online]. Available:http://www.analog.com/media/en/technicaldocumentation/data-sheets/AD620.pdf
• S. Yathunanthan, L. U. R. Chandrasena, A. Umakanthan, V. Vasuki, and S. R. Munasinghe, “Controlling a wheelchair by use of EOG signal,” Proc. 2008 4th Int. Conf. Inf. Autom. Sustain. ICIAFS 2008, no. January 2009, pp. 283–288, 2008.
• A. Úbeda, E. Iáñez, and J. M. Azorín, “Wireless and portable EOG-based interface for assisting disabled people,” IEEE/ASME Trans. Mechatronics, vol. 16, no. 5, pp. 870–873, 2011.
• A. Ayvaz, G. Ertaş, “Design of an EOG Based Electronic Circuit to Control Actuators”, Tiptekno ‘14, pp. 25–28, Kapadokya, 2014.