Boosting a weak learning algorithm by majority
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Task-evoked pupillary response to mental workload in human-computer interaction
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Towards an index of opportunity: understanding changes in mental workload during task execution
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Galvanic skin response (GSR) as an index of cognitive load
CHI '07 Extended Abstracts on Human Factors in Computing Systems
Measuring the task-evoked pupillary response with a remote eye tracker
Proceedings of the 2008 symposium on Eye tracking research & applications
Estimating cognitive load using remote eye tracking in a driving simulator
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Discriminating stress from cognitive load using a wearable EDA device
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Eye activity as a measure of human mental effort in HCI
Proceedings of the 16th international conference on Intelligent user interfaces
Pupillary response based cognitive workload index under luminance and emotional changes
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Pupillary response based cognitive workload measurement under luminance changes
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
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Pupillary response is a popular physiological index of cognitive workload that can be used for design and evaluation of adaptive interface in various areas of human-computer interaction (HCI) research. However, in practice various confounding factors unrelated to workload, including changes of luminance condition and emotional arousal might degrade pupillary response based workload measures such as commonly used mean pupil diameter. This work investigates pupillary response as a cognitive workload measure under the influence of such confounding factors. Video-based eye tracker is used to record pupillary response during arithmetic tasks under luminance and emotional changes. Machine learning based feature selection and classification techniques are proposed to robustly index cognitive workload based on pupillary response even with the influence of noisy factors unrelated to workload.