Papers for

usability testing teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Virtual reality eye trackers show hidden timing and accuracy flaws

What Mean Error Misses: Device-Clock Diagnostics for Evaluating Eye Tracking across Three Virtual Reality Headsets

Abstract: Gaze research in virtual reality needs eye trackers of known error. Thirty participants completed the same protocol on the Meta Quest Pro (QP), HTC VIVE Pro Eye (PE) and HTC VIVE Focus Vision (FV); to our knowledge, this is the first evaluation of the FV eye tracker. In a random-saccade task, median participant mean error was 1.04° on QP, 1.50° on PE and 2.41° on FV, which remained least accurate after linear recalibration. In a later block with a darker background, PE error rose by 0.70°. FV samples were positively correlated: dispersion was 9.8 times the sample-to-sample root mean square. The device clocks showed losses that mean error hides. QP paused for up to 6.04 s, and FV skipped single samples. During instructed eye closures, QP flagged every sample valid while gaze moved 9.74°. Evaluations should report repeated timestamps, loss composition on the device clock and validity during eye closures.

Fri 23 OctHuman-Computer Interaction

The gist

Measuring where people look inside virtual reality headsets is important but tricky. The authors tested eye trackers in three VR devices and found that the usual error measurements miss problems with timing and data validity. For example, some devices paused or dropped data samples without obvious warning, and one wrongly reported eye movement when eyes were closed. Their findings suggest new ways to check eye trackers more carefully beyond just average error numbers.

Open → 2610.32915v1