Affordable sensor based gaze tracking for realistic psychological assessment

Eye movement analysis finds tremendous usefulness in various medical screening applications and rehabilitation. Infrared sensor based eye trackers are becoming popular but these are expensive and need repeated calibration. Moreover, with multiple calibration also, there persists some noises called, variable and systematic, resulting in inaccurate gaze tracking. This study aims to build an one time calibration module to avoid the overhead of multiple calibration and to design an algorithm to remove both the types of errors effectively. The proposed approach is used for correcting the gaze tracking data for Digit Gazing task and standard recall-recognition test, where an accuracy of 90% and 82% are achieved respectively for detecting the gaze positions against the raw eye gaze data. Results also show that it is possible to perform accurate gaze tracking with one-time calibration method provided the experimental setup is not altered.