Evaluating the Data Quality of Eye Tracking Signals from a Virtual\n Reality System: Case Study using SMI's Eye-Tracking HTC Vive
Bibliographic record
Abstract
We evaluated the data quality of SMI's tethered eye-tracking head-mounted\ndisplay based on the HTC Vive (ET-HMD) during a random saccade task. We\nmeasured spatial accuracy, spatial precision, temporal precision, linearity,\nand crosstalk. We proposed the use of a non-parametric spatial precision\nmeasure based on the median absolute deviation (MAD). Our linearity analysis\nconsidered both the slope and adjusted R-squared of a best-fitting line. We\nwere the first to test for a quadratic component to crosstalk. We prepended a\ncalibration task to the random saccade task and evaluated 2 methods to employ\nthis user-supplied calibration. For this, we used a unique binning approach to\nchoose samples to be included in the recalibration analyses. We compared our\nquality measures between the ET-HMD and our EyeLink 1000 (SR-Research, Ottawa,\nOntario, CA). We found that the ET-HMD had significantly better spatial\naccuracy and linearity fit than our EyeLink, but both devices had similar\nspatial precision and linearity slope. We also found that, while the EyeLink\nhad no significant crosstalk, the ET-HMD generally exhibited quadratic\ncrosstalk. Fourier analysis revealed that the binocular signal was a low-pass\nfiltered version of the monocular signal. Such filtering resulted in the\nbinocular signal being useless for the study of high-frequency components such\nas saccade dynamics.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".