Journal article

Classifying Attention Types with Thermal Imaging and Eye Tracking

Yomna Abdelrahman, Anam Ahmad Khan, Joshua Newn, Eduardo Velloso, Sherine Ashraf Safwat, James Bailey, Andreas Bulling, Frank Vetere, Albrecht Schmidt

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | Association for Computing Machinery (ACM) | Published : 2019

Abstract

Despite the importance of attention in user performance, current methods for attention classification do not allow to discriminate between different attention types. We propose a novel method that combines thermal imaging and eye tracking to unobtrusively classify four types of attention: sustained, alternating, selective, and divided. We collected a data set in which we stimulate these four attention types in a user study (N = 22) using combinations of audio and visual stimuli while measuring users' facial temperature and eye movement. Using a Logistic Regression on features extracted from both sensing technologies, we can classify the four attention types with high AUC scores up to 75.7% f..

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