Move Your Wheelchair with Your Eyes

Gökçen ÇETINEL; Sevda GÜL; Zafer TIRYAKI; Enes KUZU; Meltem MILLIGÜNEY

doi:10.18100/ijamec.2017SpecialIssue30462

Research ArticlesverifiedPeer-ReviewedOpen Access

Pages: 5-8

Move Your Wheelchair with Your Eyes

Gökçen ÇETINEL •

Sevda GÜL •

Zafer TIRYAKI •

Enes KUZU •

Meltem MILLIGÜNEY

Publication DateSeptember 24, 2017

Volume / IssueVol. , No. (pp. 5-8)

DOI Identifier10.18100/ijamec.2017SpecialIssue30462

subject

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

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Move Your Wheelchair with Your Eyes

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