A-DisETrac Advanced Analytic Dashboard for Distributed Eye Tracking

Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Yasith Jayawardana, Mohan Sunkara, Andrew T. Duchowski, Vikas Ashok, S. Jayarathna
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Abstract

Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K and real-time index of pupillary activity relate to collaborative behavior. It was observed that the time a group takes to complete a puzzle is related to the ambient visual scanning behavior quantified and groups that spent more time had more scanning behavior. User experience questionnaire results suggest that their dashboard provides a comparatively good user experience.
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A-DisETrac 分布式眼动仪高级分析仪表板
了解个人在协作任务中如何集中注意力和进行视觉搜索,有助于提高用户的参与度。眼动跟踪测量为这种理解提供了信息线索。本文介绍了用于分布式眼动跟踪的高级分析仪表板 A-DisETrac。它使用现成的眼动追踪器对多个用户进行并行监控,实时计算传统和先进的注视测量值,并将其显示在交互式仪表盘上。通过两项试点研究,对该系统的用户体验和实用性进行了评估,并与现有工作进行了比较。此外,该系统还用于研究环境焦点系数 K 和瞳孔活动实时指数等高级凝视测量指标与协作行为的关系。研究发现,一个小组完成一个谜题所花费的时间与量化的环境视觉扫描行为有关,花费更多时间的小组有更多的扫描行为。用户体验问卷调查结果表明,他们的仪表盘提供了相对较好的用户体验。
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A-DisETrac Advanced Analytic Dashboard for Distributed Eye Tracking Comparison of Light Field and Conventional Near-Eye AR Displays in Virtual-Real Integration Efficiency Automation of Explainability Auditing for Image Recognition Adaptive Acquisition and Visualization of Point Cloud Using Airborne LIDAR and Game Engine iASSIST
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