LAUSR

Eye-tracking while reading is an emerging application where the goal is to track the progression of reading. The challenges for accurate tracking of the reading progression are due to the measurement noise of the eye-tracker and the rapid and uncertain movement of the eye gaze. Solutions to this problem developed in the recent past suffer from many limitations, such as the need to know the text context and the need to have a batch of one page of data for classification. In this article, we relax these assumptions and develop a novel, real-time line classification approach. The proposed solution consists of an improved slip-Kalman smoother (slip-KS) that is designed to detect new line returns and to reduce the variance in the eye-gaze measurements. After preprocessing of the data by the slip-KS, a classification approach is employed to track the lines being read in real-time. Two such classifiers are demonstrated in this article; one is based on Gaussian discriminants, and the other is based on support vector machines. The proposed approaches were tested using realistic eye-gaze data from seven participants. Analysis based on the collected data using the proposed algorithms shows significantly improved performance over existing methods.