Physics Education Research Group, Physics Department, TU Kaiserslautern, 67663 Kaiserslautern, Germany.
Mediainformatics Group, Institute of Informatics, LMU Munich, 80337 Munich, Germany.
Sensors (Basel). 2021 Nov 18;21(22):7668. doi: 10.3390/s21227668.
Remote eye tracking has become an important tool for the online analysis of learning processes. Mobile eye trackers can even extend the range of opportunities (in comparison to stationary eye trackers) to real settings, such as classrooms or experimental lab courses. However, the complex and sometimes manual analysis of mobile eye-tracking data often hinders the realization of extensive studies, as this is a very time-consuming process and usually not feasible for real-world situations in which participants move or manipulate objects. In this work, we explore the opportunities to use object recognition models to assign mobile eye-tracking data for real objects during an authentic students' lab course. In a comparison of three different Convolutional Neural Networks (CNN), a Faster Region-Based-CNN, you only look once (YOLO) v3, and YOLO v4, we found that YOLO v4, together with an optical flow estimation, provides the fastest results with the highest accuracy for object detection in this setting. The automatic assignment of the gaze data to real objects simplifies the time-consuming analysis of mobile eye-tracking data and offers an opportunity for real-time system responses to the user's gaze. Additionally, we identify and discuss several problems in using object detection for mobile eye-tracking data that need to be considered.
远程眼动追踪已经成为在线分析学习过程的重要工具。与固定眼动追踪器相比，移动眼动追踪器甚至可以将机会范围扩展到真实环境中，例如教室或实验实验室课程。然而，移动眼动追踪数据的复杂且有时是手动分析常常阻碍了广泛研究的实现，因为这是一个非常耗时的过程，并且通常不适用于参与者移动或操纵物体的实际情况。在这项工作中，我们探索了使用对象识别模型在真实的学生实验室课程中为真实对象分配移动眼动追踪数据的机会。在对三种不同卷积神经网络（CNN）的比较中，我们发现 Faster Region-Based-CNN、YOLO v3 和 YOLO v4 中，YOLO v4 与光流估计相结合，在这种设置下提供了最快的结果和最高的对象检测准确性。将注视数据自动分配给真实对象简化了移动眼动追踪数据的耗时分析，并为用户注视的实时系统响应提供了机会。此外，我们还确定并讨论了在使用对象检测进行移动眼动追踪数据时需要考虑的几个问题。
