Abstract
Eye-tracking provides invaluable insight into the cognitive activities underlying a wide range of human behaviours. Identifying cognitive activities provides valuable perceptions of human learning patterns and signs of cognitive diseases like Alzheimer’s, Parkinson’s, and autism. Also, mobile devices have changed the way that we experience daily life and become a pervasive part. This systematic review provides a detailed analysis of mobile device eye-tracking technology reported in 36 studies published in high-ranked scientific journals from 2010 to 2020 (September), along with several reports from grey literature. The review provides in-depth analysis on algorithms, additional apparatus, calibration methods, computational systems, and metrics applied to measure the performance of the proposed solutions. Also, the review presents a comprehensive classification of mobile device eye-tracking applications used across various domains such as healthcare, education, road safety, news, and human authentication. We have outlined the shortcomings identified in the literature and the limitations of the current mobile device eye-tracking technologies, such as using the front-facing mobile camera. Further, we have proposed an edge computing driven eye-tracking solution to achieve the real-time eye-tracking experience. Based on the findings, the article outlines various research gaps and future opportunities that are expected to be of significant value for improving the work in the eye-tracking domain.
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