Abstract
Eye Tracking is an important research technique used to analyze the movement of the eye and to recognize a pattern. Eye Tracking is a frequently used tool to understand the prognosis of a disease. iAOI is an Artificial Neural Network model that predicts the Area or Region of Interest viewed by a participant depending on the eye movement data. An eye-tracking experiment is conducted for participants with Parkinson’s Disease and healthy controls for visual search tasks. From the eye movements recorded a higher-order dataset based on the Area of Interest is derived. This dataset is explored to understand the underlying AOI patterns for participants with Parkinson’s Disease. This prediction from iAOI help in understanding the ability to search for a region of interest by patients suffering from Parkinson’s Disease. iAOI predicts the viewed region of interest and how it deviates from the intended Area of Interest. iAOI provides offbeat visualizations that depict the higher-order Area of Interest. By applying the ANN model for this multi-class classification, an accuracy of 83% was observed.
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