Making Sense of Mobile Eye-Tracking Data in the Real-World

Abstract

Stationary eye-tracking technology has been used extensively in human-computer interaction to both understand how humans interact with computers and as an interaction mechanism. Mobile eye-tracking technology is becoming more prevalent, yet the analysis and annotation of mobile eye-tracking data remains challenging. We present a novel human-in-the-loop approach for mobile eye-tracking data analysis that dramatically reduces resource requirements. This method incorporates human insight in a semi-automatic decision making process, leveraging both computational power and human decision making abilities. We demonstrate the accuracy of this approach with eye movement data from two real-world use cases. Average accuracy across the two environments is 82.3%. Our approach holds tremendous promise and has the potential to open the door to more robust eye movement studies in the real-world.