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Harisinghani, Anuj

University of British Columbia

There has been increasing interest in non-invasive predictors of Alzheimer’s disease (AD) as an initial screening for this condition. Previously, successful attempts leveraged eye-tracking and language data generated during picture narration and reading tasks. These results were obtained with high-end, expensive eye-trackers. We explore classification using eye-tracking data collected with a webcam, where our classifiers are built using a deep-learning approach. Our results show that the webcam gaze classifier is not as good as the classifier based on high-end eye-tracking data, meaning its AU-ROC, Sensitivity and Specificity are significantly lower. However, the webcam-based classifier still beats a majority-class baseline classifier in terms of AU-ROC, indicating that predictive signals can be extracted from webcam gaze tracking. Our results provide an encouraging proof of concept that webcam gaze tracking should be further explored as an affordable alternative to high-end eye-trackers for the detection of AD.

Text

2023-03-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83892

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/