Inferring user tasks in pedestrian navigation from eye movement data in real-world environments

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.

KEYWORDS:

• Wayfinding
• random forests
• task inference
• eye tracking
• machine learning

Acknowledgments

The authors would like to thank all the reviewers for their helpful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by National Key Research and Development Program of China [Grant No. 2017YFB0503602], National Natural Science Foundation of China [NSFC, Grant No. 41471382], Beijing Normal University Teaching Construction and Reform Project [Grant No. 14-07-01], and China Scholarship Council [Grant No. 201606040097].