Gaze Behaviour on Interacted Objects during Hand Interaction in Virtual Reality for Eye Tracking Calibration

In this paper, we investigate the probability and timing of attaining gaze fixations on interacted objects during hand interaction in virtual reality, with the main purpose for implicit and continuous eye tracking re-calibration. We conducted an evaluation with 15 participants in which their gaze was recorded while interacting with virtual objects. The data was analysed to find factors influencing the probability of fixations at different phases of interaction for different object types. The results indicate that 1) interacting with stationary objects may be favourable in attaining fixations to moving objects, 2) prolonged and precision-demanding interactions positively influences the probability to attain fixations, 3) performing multiple interactions simultaneously can negatively impact the probability of fixations, and 4) feedback can initiate and end fixations on objects

Eye&Head:Synergetic Eye and Head Movement for Gaze Pointing and Selection

Eye gaze involves the coordination of eye and head movement to acquire gaze targets, but existing approaches to gaze pointing are based on eye-tracking in abstraction from head motion. We propose to leverage the synergetic movement of eye and head, and identify design principles for Eye&Head gaze interaction. We introduce three novel techniques that build on the distinction of head-supported versus eyes-only gaze, to enable dynamic coupling of gaze and pointer, hover interaction, visual exploration around pre-selections, and iterative and fast confirmation of targets. We demonstrate Eye&Head interaction on applications in virtual reality, and evaluate our techniques against baselines in pointing and confirmation studies. Our results show that Eye&Head techniques enable novel gaze behaviours that provide users with more control and flexibility in fast gaze pointing and selection

Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality

[EN] Fixation identification is an essential task in the extraction of relevant information from gaze patterns; various algorithms are used in the identification process. However, the thresholds used in the algorithms greatly affect their sensitivity. Moreover, the application of these algorithm to eye-tracking technologies integrated into head-mounted displays, where the subject's head position is unrestricted, is still an open issue. Therefore, the adaptation of eye-tracking algorithms and their thresholds to immersive virtual reality frameworks needs to be validated. This study presents the development of a dispersion-threshold identification algorithm applied to data obtained from an eye-tracking system integrated into a head-mounted display. Rules-based criteria are proposed to calibrate the thresholds of the algorithm through different features, such as number of fixations and the percentage of points which belong to a fixation. The results show that distance-dispersion thresholds between 1-1.6 degrees and time windows between0.25-0.4s are the acceptable range parameters, with 1 degrees and0.25s being the optimum. The work presents a calibrated algorithm to be applied in future experiments with eye-tracking integrated into head-mounted displays and guidelines for calibrating fixation identification algorithmsWe thank Pepe Roda Belles for the development of the virtual reality environment and the integration of the HMD with Unity platform. We also thank Masoud Moghaddasi for useful discussions and recommendations.Llanes-Jurado, J.; Marín-Morales, J.; Guixeres Provinciale, J.; Alcañiz Raya, ML. (2020). Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality. Sensors. 20(17):1-15. https://doi.org/10.3390/s20174956S1152017Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). 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Proceedings of the 2008 symposium on Eye tracking research & applications - ETRA ’08. doi:10.1145/1344471.1344500Vive Pro Eyehttps://www.vive.com/us