Year: 2018
Type: conference-paper
Abstract: Eye movement data convey a wealth of information that can be used to inspect human behavior and cognitive processes. In this study, we explored to identify user tasks in map-based pedestrian navigation using eye movement data. We collected 44 participants’ eye movement data by conducting an eye tracking experiment in real-world environments. We trained and cross-validated a Random Forests classifier to classify 7 common user tasks using 564 features. The preliminary results show the classifier can achieve an overall accuracy of 48%.
Authors: Liao, Hua, Dong, Weihua, Gartner, Georg, Liu, Huiping
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2783027375