Fig 1.
Two types of errors associated with eye trackers, (a) Variable and (b) Systematic error.
Fig 2.
Schema of the designed calibration stimulus with the calibration point having diameter 0.657° and moving at a constant speed of 1.92°/sec on the screen.
Table 1.
Details of the test stimulus used for the study.
Fig 3.
Designed recall-recognition (RR) test (a) 6 words for recall (3 words/column); and (b) 12 words (6 words/column) (c) 24 words (12 words/column) and (d) 32 words (16 words/column) for recognition.
Fig 4.
Number gazing (NG) task for inter-digit spacing of 100 pixels.
Fig 5.
Block-diagram of the proposed method.
Fig 6.
Supervised approach for data correction.
Fig 7.
Unsupervised approach for data correction.
Fig 8.
Demonstration of inverse weighing function for 4 nearest neighbor stimuli points.
Fig 9.
Experimental setup with the eye tracker at the bottom of the display and a chin rest.
Fig 10.
State of the art methods considered for comparison for different types of error, where, LF = Low pass filtering, KF = Kalman filtering, LT = Linear Transformation and CS = Closest Stimulus based approach.
Fig 11.
Comparison of different filtering approaches for the NG task wherein the participant gazed at 4 different numbers.
Here, LF = Low pass filtering, KF = Kalman filtering, GSP + KF = Graph signal processing and Kalman filter.
Fig 12.
Demonstration of different filtering approaches in terms of smoothness for NG task.
Note that for the gaze chunk on the digit ‘1’, the values of SR in terms of degrees are 0.932°, 0.92°, 0.26°, 0.2°, respectively for raw data, LF, KF and GSP + KF approaches. Here, LF = Low pass filtering, KF = Kalman filtering, GSP + KF = Graph signal processing and Kalman filter.
Fig 13.
Smoothness ratio of proposed and existing methods in NG task.
Here, LF = Low pass filtering, KF = Kalman filtering, GSP + KF = Graph signal processing and Kalman filter.
Fig 14.
Smoothness ratio of proposed and existing methods in the RR task.
Here, LF = Low pass filtering, KF = Kalman filtering, GSP + KF = Graph signal processing and Kalman filter.
Fig 15.
Closeness measure results for variable error correction of the NG task for different spacing (for one-time calibration protocol).
Here, LF = Low pass filtering, KF = Kalman filtering, GSP + KF = Graph signal processing and Kalman filter.
Fig 16.
Closeness measure results for variable error correction of the RR task for different number of words (for one-time calibration protocol).
Here, LF = Low pass filtering, KF = Kalman filtering, GSP + KF = Graph signal processing and Kalman filter.
Fig 17.
Pictorial scheme of different types of boundaries defined around each number in the NG task.
Table 2.
Accuracy (%) of detecting the gazed number using the 3 different boundaries for raw gaze data.
Fig 18.
Accuracy of detecting the gazed numbers using different algorithmic chains for the NG task (for one-time calibration protocol).
Fig 19.
Accuracy of detecting the gazed words using different algorithmic chains in the RR task (for one-time calibration protocol).
Table 3.
Comparison of average accuracy (%) in systematic error correction for different calibration protocol (RR-Recall recognition, NG-Number gaze).
Fig 20.
Removal of variable error for participant P1.
(a) Smoothness parameter, (b) Closeness parameter.
Fig 21.
Removal of variable error for participant P2.
(a) Smoothness parameter, (b) Closeness parameter.
Table 4.
Comparison of variable error correction in terms of closeness measure and smoothness ratio in short and long duration task using GSP + KF.
Table 5.
Comparison of average accuracy (%) in systematic error correction for short and long duration task.