PURPOSE Eye gaze tracking is proving to be beneficial in many biomedical applications. The performance of systems based on eye gaze tracking is very much dependent on how accurate their calibration is. It has been reported that the gaze tracking accuracy deteriorates cumulatively and significantly with usage time. This impedes the wide use of gaze tracking in user interfaces. METHODS Explicit re-calibration, typically requiring the user's active attention, is time-consuming and can interfere with the user's main activity. Therefore, we propose an implicit re-calibration method, which can rectify the deterioration of the gaze tracking accuracy without bringing about the user's deliberate attention. We make use of hand-eye coordination, with the reasonable assumption that the eye gaze follows the pointer during a selection task, to acquire additional calibration points during normal usage of a gaze-contingent system. We construct a statistical model for the calibration and the hand-eye coordination and apply the Gaussian process regression framework to perform the re-calibration. RESULTS To validate our model and method, we performed a user study on ultrasonography tasks on a gaze-contingent interface for ultrasound machines. Results suggest that our method can rectify the tracking accuracy deterioration for [Formula: see text] of all cases where deterioration occurs in our user study. With another benchmark dataset, our method can redress tracking accuracy to a level comparable to the initial calibration in more than [Formula: see text] of the cases. CONCLUSIONS Our implicit re-calibration method is a practical and convenient fix for tracking accuracy deterioration in gaze-contingent user interfaces, and in particular for gaze-contingent ultrasound machines.

中文翻译：

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基于手眼协调的隐式重新校准方法，用于超声机器上的凝视跟踪：一种统计方法。

目的事实证明，视线追踪在许多生物医学应用中都是有益的。基于眼睛注视跟踪的系统的性能在很大程度上取决于其校准的准确性。据报道，凝视追踪的精度随着使用时间的增加而累积恶化。这阻碍了注视跟踪在用户界面中的广泛使用。方法明确的重新校准通常需要用户的积极关注，这既耗时又会干扰用户的主要活动。因此，我们提出了一种隐式的重新校准方法，该方法可以纠正注视跟踪精度的下降，而不会引起用户的故意注意。我们利用手眼协调，合理地假设在选择任务期间眼睛注视着指针，在正常使用凝视视力系统期间获取其他校准点。我们构建用于校准和手眼协调的统计模型，并应用高斯过程回归框架执行重新校准。结果为了验证我们的模型和方法，我们在超声机器的凝视或偶然界面上对超声任务进行了用户研究。结果表明，在我们的用户研究中，对于所有发生劣化的情况，我们的方法都可以纠正[公式：请参见文本]的跟踪精度劣化。使用另一个基准数据集，我们的方法可以将跟踪精度纠正到与初始校准相当的水平，而不仅仅是这些案例。