The impact of slippage on the data quality of head-worn eye trackers

Diederick C. Niehorster*, Thiago Santini, Roy S. Hessels, Ignace T.C. Hooge, Enkelejda Kasneci, Marcus Nyström

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant’s head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs’ Pupil in 3D mode, and (iv) Pupil-Labs’ Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8–3.1 increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup.

Original languageEnglish
Pages (from-to)1140-1160
Number of pages21
JournalBehavior Research Methods
Volume52
Issue number3
DOIs
Publication statusPublished - 1 Jun 2020

Keywords

  • Data quality
  • Eye movements
  • Head-mounted eye tracking
  • Mobile eye tracking
  • Natural behavior
  • Wearable eye tracking

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