Gaze-tracking analysis for cognitive screening and assessment

Abstract

Neurodegenerative diseases degrade the mental and physical capabilities of afflicted individuals around the world. Early diagnosis can make it possible to reduce the effects and progression of these diseases. The novel digital Symbol Digit Test (dSDT) is a new cognitive test that judges patterns of recall and cognitive associations, which can be used to differentiate between cognitive signs displayed by normal and neurologically impaired subjects. Our research identifies different strategies of learning and recall, and automates the process of analyzing the eye-tracking data collected from the dSDT to detect these patterns. This work paves the foundation for future studies to assess differences between healthy and impaired individuals, and model these features to detect and aid in the diagnosis of cognitive states of individuals.