Accelerating eye movement research via accurate and affordable smartphone eye tracking

Nachiappan Valliappan, Na Dai, Ethan Steinberg, Junfeng He, Kantwon Rogers, Venky Ramachandran, Pingmei Xu, Mina Shojaeizadeh, Li Guo, Kai Kohlhoff and Vidhya Navalpakkam ()
Additional contact information
Nachiappan Valliappan: Google Research
Na Dai: Google Research
Ethan Steinberg: Google Research
Junfeng He: Google Research
Kantwon Rogers: Google Research
Venky Ramachandran: Google Research
Pingmei Xu: Google Research
Mina Shojaeizadeh: Google Research
Li Guo: Google Research
Kai Kohlhoff: Google Research
Vidhya Navalpakkam: Google Research

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18360-5

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DOI: 10.1038/s41467-020-18360-5

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