Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Classifying Attention Types with Thermal Imaging and Eye Tracking
AU - Abdelrahman, Yomna
AU - Khan, Anam Ahmad
AU - Newn, Joshua
AU - Velloso, Eduardo
AU - Safwat, Sherine Ashraf
AU - Bailey, James
AU - Bulling, Andreas
AU - Vetere, Frank
AU - Schmidt, Albrecht
PY - 2019/9/9
Y1 - 2019/9/9
N2 - 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.
AB - 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.
U2 - 10.1145/3351227
DO - 10.1145/3351227
M3 - Journal article
VL - 3
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
SN - 2474-9567
IS - 3
M1 - 69
ER -