Eye Movement Recognition Using Support Vector Machine
People with disabilities suffer from inability to communicate with their surroundings, so Human-
Computer Interaction (HCI) technologies are used to have a means of communication for people with
disabilities with their surroundings. HCI is an emerging technology in the disciplines of Artificial
Intelligence and Biomedical Engineering. To power an external device, HCI technology uses several
basic signals such as ECG, EMG, and EEG. Electrooculography (EOG) is a technique for measuring the
potential difference between the cornea and the retina located between the front and back of the human
eye, and the main application of EOG is to determine the directions of different eye movements. This
study aims to assess eye movement for communication by persons with disabilities using
electrocardiogram (EOG) data. In this study, the Supporting Vector Machine (SVM) classification
technique was used and two types of features (statistical and time domain features) were used.
Classification accuracy was 90.7% and 93.9% when using SVM with statistical domain and time domain
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