Improving Data Quality from Remote Eye Tracking Systems Using Real Time Feedback

This study proposes a solution to improve data quality from remote desktop eye trackers. Poor data quality from these systems regularly occurs as a result of participants unknowingly moving outside of the functional data collection area, i.e. the eye tracking box. Researchers are often not aware of the low quality data until after it has been recorded. As a result potentially large amounts of data are unusable. To alleviate this concern, we propose a real-time feedback system that alerts participants when poor eye tracking data are detected, thus enabling them to adjust their position in front of the eye tracker as soon as they move out of the functional data collection area. This capability allows researchers to acquire a higher percentage of useful data over the course of an experiment. Our approach utilized a Raspberry Pi that collected and interpreted data quality from an eye tracker in real time. Data quality from each eye was mapped to a light emitting diode (LED) placed above the computer monitor. The color of LED reflected the current quality of eye tracking data with green and red indicating high and low quality respectively. To determine if the system was effective, we compared the data quality for participants who used the system relative to participants who did not while they performed a cognitive task. Results show increased data quality for those participants using the feedback system. Our results suggest that future studies using remote desktop eye trackers can increase data quality by providing real-time data quality feedback to the participants.