@inproceedings{3ab2baffd9904cffbaa1d8f1461ad8e2,
title = "Monitoring response quality during campimetry via eye-tracking",
abstract = "In a variety of use-cases, deriving information on user's fatigue is an important step for content adaptation. In this work, we investigate which eye-tracking related measures can predict the error rate (as a proxy of subject's fatigue) during a visual experiment. Data was collected during a 40 minutes campimetric task, where the user has to detect visual stimuli (i.e., dots) of different contrast. We found that eye-tracking measures can be used to train a machine learning model to predict the error rate of a user with an average correlation of 0.72±0.17. The results show that this method can be used to measure the user's response quality. Copyright is held by the owner/author(s).",
keywords = "Blink rate, Campimetry, Eye-tracking, Fatigue, Pupil diameter, Vigilance",
author = "{Vergani Dambros}, Gustavo and Judith Ungewiss and K{\"u}bler, {Thomas C.} and Enkelejda Kasneci and Martin Sp{\"u}ler",
year = "2017",
month = mar,
day = "7",
doi = "10.1145/3030024.3038268",
language = "English",
series = "International Conference on Intelligent User Interfaces, Proceedings IUI",
publisher = "Association for Computing Machinery",
pages = "61--64",
booktitle = "IUI 2017 - Companion of the 22nd International Conference on Intelligent User Interfaces",
note = "22nd International Conference on Intelligent User Interfaces, IUI 2017 ; Conference date: 13-03-2017 Through 16-03-2017",
}