Inferring user tasks in pedestrian navigation from eye movement data in real-world environments |
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| Authors: | Hua Liao Haosheng Huang Georg Gartner Huiping Liu |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, and Faculty of Geographical Science, Beijing Normal University, Beijing, China;2. Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria;3. GIScience Center, Department of Geography, University of Zurich, Zurich, Switzerland;4. Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria |
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| Abstract: | Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing. |
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| Keywords: | Wayfinding random forests task inference eye tracking machine learning |
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