Date of Award

7-25-2019

Publication Type

Master Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

First Advisor

Balasingam, B.

Keywords

Estimation, Eye-gaze tracking, Hidden Markov Models, Human Machine Systems, Kalman Filters, Statistical Modeling

Rights

info:eu-repo/semantics/embargoedAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

In this thesis, we investigate methods to accurately track reading progression by analyzing eye-gaze fixation points, using commercially available eye tracking devices and without the imposition of unnatural movement constraints. In order to obtain the most accurate eye-gaze fixation point data possible, the current state of the art relies on expensive, cumbersome apparatuses. Eye-gaze tracking using less expensive hardware, and without constraints imposed on the individual whose gaze is being tracked, results in less reliable, noise-corrupt data which proves difficult to interpret. Extending the accessibility of accurate reading progression tracking beyond its current limits and enabling its feasibility in a real-world, constraint-free environment will enable a multitude of futuristic functionalities for educational, enterprise, and consumer technologies. We first discuss the ``Line Detection System'' (LDS), a Kalman filter and hidden Markov model based algorithm designed to infer from noisy data the line of text associated with each eye-gaze fixation point reported every few milliseconds during reading. This system is shown to yield an average line detection accuracy of 88.1\%. Next, we discuss a ``Horizontal Saccade Tracking System'' (HSTS) which aims to track horizontal progression within each line, using a least squares approach to filter out noise. Finally, we discuss a novel ``Slip-Kalman'' filter which is custom designed to track the progression of reading. This method improves upon the original LDS, performing at an average line detection accuracy of 97.8\%, and offers advanced capability in horizontal tracking compared to the HSTS. The performance of each method is demonstrated using 25 pages worth of data collected during reading

Available for download on Friday, October 09, 2020

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