Classifying Attention Types with Thermal Imaging and Eye Tracking

Abdelrahman, Yomna and Khan, Anam Ahmad and Newn, Joshua and Velloso, Eduardo and Safwat, Sherine Ashraf and Bailey, James and Bulling, Andreas and Vetere, Frank and Schmidt, Albrecht (2019) Classifying Attention Types with Thermal Imaging and Eye Tracking. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3 (3). ISSN 2474-9567

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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% for the user independent-condition independent, 87% for the user-independent-condition dependent, and 77.4% for the user-dependent prediction. Our findings not only demonstrate the potential of thermal imaging and eye tracking for unobtrusive classification of different attention types but also pave the way for novel applications for attentive user interfaces and attention-aware computing.

Item Type:
Journal Article
Journal or Publication Title:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
ID Code:
165787
Deposited By:
Deposited On:
09 Feb 2022 09:40
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Mar 2022 03:58