Real-time Prediction of User Performance based on Pupillary Assessment via Eye Tracking

Faculty/Professorship: Information Systems and Services  
Author(s): Buettner, Ricardo; Sauer, Sebastian; Maier, Christian ; Eckhardt, Andreas
Title of the Journal: AIS Transactions on Human-Computer Interaction
ISSN: 1944-3900
Publisher Information: AIS eLibrary
Year of publication: 2018
Volume: 10
Issue: 1
Pages: 26-56
Language(s): English
DOI: 10.17705/1thci.00103
We propose a method to predict user performance based on eye-tracking. The method uses eye-tracking-based pupillometry to capture pupil diameter data and calculates -based on a Random Forest algorithm - user performance expectations. We conducted a large-scale experimental evaluation (125 participants aged from 21 to 61 years) and found promising results that pave the way for a dynamic real-time adaption of IT to a user's mental effort and expected user performance. We have already achieved a good classification accuracy of user performance after only 40 seconds (5% of the mean total trial time that our participants took to complete our experiment). The non-invasive contact-free method can be applied cost-efficiently both in research and practical environments.
Type: Article
Year of publication: 7. January 2019