Barz Michael, Bhatti Omair Shahzad, Alam Hasan Md Tusfiqur, Nguyen Duy Minh Ho, Altmeyer Kristin, Malone Sarah, Sonntag Daniel
Interactive Machine Learning, German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, Germany;
Applied Artificial Intelligence, University of Oldenburg, 26129 Oldenburg, Germany.
J Eye Mov Res. 2025 Jul 7;18(4):27. doi: 10.3390/jemr18040027. eCollection 2025 Aug.
Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations' validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals.
移动眼动追踪是心理学和以用户为中心的交互设计中的一项重要工具，用于理解人们如何处理视觉场景和用户界面。然而，分析头戴式眼动追踪仪的记录（通常包括场景的自我中心视角视频和注视信号）是一个耗时且主要靠人工的过程。为应对这一挑战，我们开发了eyeNotate，这是一个基于网络的注释工具，可实现半自动数据注释，并能从用户的纠正反馈中学习改进。用户可以在视频编辑风格的界面（基线版本）中手动将注视事件映射到感兴趣区域（AOI）。此外，我们的工具可以基于少样本图像分类模型（IML支持版本）生成注视到AOI的映射建议。我们与训练有素的注释者（n = 3）进行了一项专家研究，以比较基线版本和IML支持版本。我们在数据注释任务中测量了感知可用性、注释的有效性和可靠性以及效率。我们要求参与者使用现有数据集（n = 48）对来自单个个体的数据进行重新注释。此外，我们进行了一次半结构化访谈，以了解参与者如何使用所提供的IML功能并评估我们的设计决策。在一项事后实验中，我们研究了三种图像分类模型在注释其余47个个体的数据时的性能。
