Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI's Eye-Tracking HTC Vive
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Abstract (arXiv)
We evaluated the data quality of SMI's tethered eye-tracking head-mounted display based on the HTC Vive (ET-HMD) during a random saccade task. We measured spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. We proposed the use of a non-parametric spatial precision measure based on the median absolute deviation (MAD). Our linearity analysis considered both the slope and adjusted R-squared of a best-fitting line. We were the first to test for a quadratic component to crosstalk. We prepended a calibration task to the random saccade task and evaluated 2 methods to employ this user-supplied calibration. For this, we used a unique binning approach to choose samples to be included in the recalibration analyses. We compared our quality measures between the ET-HMD and our EyeLink 1000 (SR-Research, Ottawa, Ontario, CA). We found that the ET-HMD had significantly better spatial accuracy and linearity fit than our EyeLink, but both devices had similar spatial precision and linearity slope. We also found that, while the EyeLink had no significant crosstalk, the ET-HMD generally exhibited quadratic crosstalk. Fourier analysis revealed that the binocular signal was a low-pass filtered version of the monocular signal. Such filtering resulted in the binocular signal being useless for the study of high-frequency components such as saccade dynamics.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2023Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challengescited by 12
- 2021Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Datasetcited by 11
- 2024Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipelinecited by 2
- 2025QualitEye: Public and Privacy-preserving Gaze Data Quality Verificationcited by 1
- 2024Gaze Prediction as a Function of Eye Movement Type and Individual Differencescited by 0
- 2026EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesiscited by 0
- 2026EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis3 Sep 2026
- 2025QualitEye: Public and Privacy-preserving Gaze Data Quality Verification6 Jun 2025
- 2024Gaze Prediction as a Function of Eye Movement Type and Individual Differences31 Dec 2024
- 2024Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipeline26 Feb 2024
- 2023Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges23 May 2023
- 2021Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset21 Apr 2021