A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements
This provides a method to quantify reading progression for applications in education or accessibility, but it appears incremental as it builds on existing eye-tracking and filtering techniques.
The paper tackles the problem of tracking eye-gaze progression during reading using a novel Slip-Kalman filter, achieving accurate quantification of reading behaviors such as identifying read/skipped lines and estimating time per line based on data from 25 pages collected with a commercial eye-tracking device.
In this paper, we propose an approach to track the progression of eye-gaze while reading a block of text on computer screen. The proposed approach will help to accurately quantify reading, e.g., identifying the lines of text that were read/skipped and estimating the time spent on each line, based on commercially available inexpensive eye-tracking devices. The proposed approach is based on a novel Slip Kalman filter that is custom designed to track the progression of reading. The performance of the proposed method is demonstrated using 25 pages eye-tracking data collected using a commercial desk-mounted eye-tracking device.