Eye-tracking Technologies in Mobile Devices Using Edge Computing: A Systematic Review

Abstract

Eye-tracking provides invaluable insight into the cognitive activities underlying a wide range of human behaviours. Identifying cognitive activities provide valuable perceptions of human learning patterns and signs of cognitive diseases like Alzheimer’s, Parkinson’s, 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 paper outlines various research gaps and future opportunities that are expected to be of significant value for improving the work in the eye-tracking domain.

Optical and motor changes associated with lighting and near vision tasks in electronic devices

“…The outgoing wavefront is divided into several beams by an array of microlenslets, which produce spot images on a video sensor. The shape of the wavefront is determined by the displacement of each spot from the matching nonaberrated reference location (Gunawardena et al, 2022;Liang et al, 1994). After blinking, measurements were taken focusing on the Purkinje images obtained by aligning the instrument axis with the eye's pupil (axial conjugation between an instrument 32x32 lenslet array and the eye's pupillary plane).…”

“…Nowadays, research with eye trackers is widespread due to the many objective parameters that can be measured. Specifically, recording reading tasks on electronic devices is gaining importance in objectively analyzing ocular motility (Feis et al, 2021;Gunawardena et al, 2022;Siegenthaler et al, 2012).…”

Augment-Me: An Approach for Enhancing Pilot’s Helmet-Mounted Display Visualization for Tactical Combat Effectiveness and Survivability

“…Pupil-cornea reflection measurements are performed in real-time, while the image processing algorithm provides an accurate measurement of the gaze. The appearance model updated only would function as a classifier while the filter is used to predict the search region and the fixation state can be modeled from Markov decision models, Cumulative Sum algorithms, Deep Learning and Machine Learning (ML) processes and Edge computing [52][53][54]. Edge computing can promote multimodal interaction with improved pilot-cockpit integration beyond standard interfaces with a high level of speed and accuracy [55].…”