Publication

Abstract : With an increase in eye-tracking applications, there is a need for eye gaze data analytics to widen its scope to provide customized solutions. However, among several types of analytics such as predictive, descriptive, diagnostic, and prescriptive analytics providing insight into it is quite challenging. This work presents the need and role of prescriptive data analysis that can be carried out on eye gaze data pertaining to a specific study of research on the diagnosis of Parkinson’s disease patients during visual search and visual attention tasks. We further look at the various aspects like the image stimulus used, a task performed by the viewer, and its relation to the viewer’s eye movement behavior. iSTIMULI includes the design of the image stimuli used to classify the eye movements of Parkinson’s disease patients from healthy controls. Visualizations of eye movements on the image stimulus designed are presented. A machine learning analysis is also provided that substantiates the significance of the stimuli with the highest f-measure of 73%. iSTIMULI establishes a relation between image stimulus and viewer behavior. As a prescriptive analysis model, iSTIMULI calculates the risk based on eye movements and recommends actions to be taken in the future to improve the ability of the participant. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.