Just as there are numerous ways to cook an egg, there are numerous ways to recreate a YouTube video of cooking an omelette. We created a dataset of 10 academics replicating a viral video of making an omelette. We evaluated the saccade behavior during the varying subtasks and found differences related to the actions (whisking, sprinkling, etc.) and the objects (eggs, butter, plate, etc.). This dataset can further offer insight into eye movements in complex tasks and is potentially even applicable for task planning and intention prediction. The available data can be found at https://zenodo.org/doi/10.5281/zenodo.10875267.

Eye tracking enables the implementation of gaze-contingent visual impairment simulations, holding promise for research, clinical uses, and demonstrations. While head-mounted displays (HMDs) are common for such simulations, screen-based implementations can be an alternative for individuals who are reluctant or unable to use HMDs. Screen-based simulations are less likely to induce virtual reality sickness and allow eye contact and social interaction with the demonstrator or examiner. To improve the immersion, we use mobile eye tracking with fiducial markers for a head- and gaze-contingent simulation of visual impairments on a screen.

Neurological degenerative conditions can affect motor functions, making mobility daunting. Recent configurations of mobility devices that leverage artificial intelligence (AI) show its ability to handle complex information like user input. We create a virtual reality environment to measure participants’ reactions to correct and incorrect feedback from an AI-assistance system. Using gaze to evaluate these reactions, we investigate whether we can automatically predict an upcoming system error. Our results show that gaze reactions occur within 300 ms when the system highlights user input, but the delay extends to 1 second without highlighting. Subject dependent gaze behavior proved complicated for developing a generalizable model based on previous work using TCNs for online recognition of upcoming errors. Therefore, more adaptable models for individuals may be a better alternative for gaze-based accessibility systems.

This paper is about a small eye tracking study for scan path classification. Seven participants played Mario Kart while wearing a head mounted eye tracker. In total, we had 64 recordings, but one had to be removed (Only 79 gaze samples were recorded). We compared different scan path classification features to estimate the performance of the participants based on the ranking they achieved. The best performing feature was ENCODJI which incooperates saccades and the heatmap in one feature. HOV, which uses saccade angles, performed well for all tasks but was outperformed by the heatmap (HEAT) for the last two groups.