Filtering on the Go: Effect of Filters on Gaze Pointing Accuracy During Physical Locomotion in Extended Reality
Why this work is in the frame
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Bibliographic record
Abstract
Eye tracking filters have been shown to improve accuracy of gaze estimation and input for stationary settings. However, their effectiveness during physical movement remains underexplored. In this work, we compare common online filters in the context of physical locomotion in extended reality and propose alterations to improve them for on-the-go settings. We conducted a computational experiment where we simulate performance of the online filters using data on participants attending visual targets located in world-, path-, and two head-based reference frames while standing, walking, and jogging. Our results provide insights into the filters' effectiveness and factors that affect it, such as the amount of noise caused by locomotion and differences in compensatory eye movements, and demonstrate that filters with saccade detection prove most useful for on-the-go settings. We discuss the implications of our findings and conclude with guidance on gaze data filtering for interaction in extended reality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it