Background. Eye-controlled interfaces can support communication and selection tasks when physical interaction is difficult, but conventional multi-point calibration may be burdensome for users who fatigue quickly or have severe motor limitations. This study operationally evaluates a low-burden single-point adaptive gaze-to-cursor correction pipeline for a dwell-based selection interface. Methods. The pipeline combines central single-point offset estimation, robust median/median absolute deviation (MAD) sample handling, recorded-stream jump rejection, exponential moving average filtering, dwell-based selection, and post-selection offset adaptation. It was evaluated using application-level gaze-to-cursor coordinates from 43 participants across 129 sessions. The primary endpoint was target-proximity Root Mean Square (RMS) during automatically logged dwell-active selection periods. Because these periods were defined by the pipeline’s online state, the endpoint was algorithm-conditioned. Whole-quiz RMS was analyzed as a secondary full-trajectory descriptor rather than as independent validation. Results. Averaged across three repeated measurements, target-proximity RMS during dwell-active periods decreased from 193.40 pixels (px) (approximately 3.15°) for the recorded uncorrected coordinates to 56.02 px (0.91°) for the complete pipeline output. The mean reduction was 137.38 px (95% confidence interval (CI), 116.59–158.17 px; Cohen’s dz = 2.03), and all 43 participants showed a reduction. Conclusions. The results support the practical feasibility of the complete correction pipeline for target localization in the evaluated dwell-based interface. They do not independently validate native eye-tracker accuracy or establish superiority over conventional multi-point calibration.
Krowicki et al. (Sun,) studied this question.