Abstract
Eye tracking is one of the most widely used technique for assessment, screening and human-machine interaction related applications. There are certain issues which limit the usage of eye trackers in practical scenarios, viz., i) need to perform multiple calibrations and ii) presence of inherent noise in the recorded data. To address these issues, we have proposed a protocol for one-time calibration against the “regular” or the “multiple” calibration phases. It is seen that though it is always desirable to perform multiple calibration, the one-time calibration also produces comparable results and might be better for individuals who are not able to perform multiple calibrations. In that case, “One-time calibration” can also be done by a participant and the calibration results are used for the rest of the participants, provided the chin rest and the eye tracker positions are unaltered. The second major issue is the presence of the inherent noise in the raw gaze data, leading to systematic and variable errors. We have proposed a signal processing chain to remove these two types of errors. Two different psychological stimuli-based tasks, namely, recall-recognition test and number gazing task are used as a case study for the same. It is seen that the proposed approach gives satisfactory results even with one-time calibration. The study is also extended to test the effect of long duration task on the performance of the proposed algorithm and the results confirm that the proposed methods work well in such scenarios too.
Highlights
In recent years, eye tracking is gaining huge importance for diagnosis and screening [1] of various medical conditions, home-based rehabilitation [2] and human-computer applications [3] due to its unobtrusive nature
The performance evaluation of our proposed methods are done in terms of algorithm/ approaches for the following scenarios 1. variable error removal techniques 2. comparison of supervised and unsupervised approaches for systematic error removal 3. comparison of single calibration against multiple calibration protocols 4. evaluation of proposed noise removal method for long duration tasks
For the gazed number 7, the radius of Kalman filter KF filtered data is slightly larger than the graph signal processing (GSP) + KF filtered data
Summary
Eye tracking is gaining huge importance for diagnosis and screening [1] of various medical conditions, home-based rehabilitation [2] and human-computer applications [3] due to its unobtrusive nature. Eye tracking is an important method for analyzing different cognitive functions [4] associated with variety of tasks like reading, writing, visual searching, driving and so on. Non-invasive eye trackers can be used to study infant cognition [5] in unconstrained, naturalistic environment. The accuracy or the robustness of such applications mostly relies on the quality of the data collected. Noisy eye movement data leads to misleading interpretations and outcomes.
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