MLGaze：消费者眼动追踪系统中基于机器学习的凝视错误模式分析
MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems
摘要：
分析眼动仪的注视精度特性是一项关键任务，因为其注视数据经常受到各种消费者眼动仪应用中的非理想操作条件的影响。在这项研究中，商业眼动跟踪设备产生的注视错误模式在机器学习算法（如分类器和回归模型）的帮助下进行了研究。凝视数据是在多种条件下从一组参与者中收集的，这些条件通常会影响在桌面和手持平台上操作的眼动仪。这些条件（这里称为误差源）包括用户距离、头部姿势和眼睛跟踪器姿势变化，并且所收集的注视数据被用于训练分类器和回归模型。可以看出，虽然通过目视检查或数据统计几乎不可能区分不同误差源对注视数据特征的影响，但机器学习模型成功地识别了不同误差源的影响并预测了由于这些条件而导致的注视误差水平的变化。本研究的目的是调查机器学习方法对注视错误模式的检测和预测的有效性，这将使我们能够深入了解眼动仪在不受约束的操作条件下的数据质量和可靠性。本研究中采用的所有机器学习方法的编码资源都包含在一个名为MLGaze的开放存储库中，以允许研究人员使用来自他们自己的眼动仪的数据复制本文中提出的原理。
英文摘要：
Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
