We propose a novel algorithm to evaluate and mitigate the uncertainty of data reported by eye gaze tracking devices embedded in virtual reality head-mounted displays. Our algorithm first is calibrated by leveraging unit quaternions to encode angular differences between reported and ground-truth gaze directions, then interpolates these quaternions for each gaze sample, and finally corrects gaze directions by rotating them using interpolated quaternions. The real part of the interpolated quaternion is used as the certainty factor for the corresponding gaze direction sample. The proposed algorithm is implemented in the VRSciVi Workbench within the ontology-driven SciVi visual analytics platform and can be used to improve the eye gaze tracking quality in different virtual reality applications including the ones for Digital Humanities research. The tests of the proposed algorithm revealed its capability of increasing eye tracking accuracy by 25% and precision by 32% compared with the raw output of the Tobii tracker embedded in the Vive Pro Eye head-mounted display. In addition, the certainty factors calculated help to acknowledge the quality of reported gaze directions in the subsequent data analysis stages. Due to the ontology-driven software generation, the proposed approach enables high-level adaptation to the specifics of the experiments in virtual reality.

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