CAREER: Eye Tracking Streaming Analytics

该项目的主要目标是使用眼动追踪数据的实时处理来获得非侵入性测量，从而揭示个体受试者注意力的客观和可量化信息（例如，能够选择有用的信息）。通过该项目取得的研究进展将用于改善神经多样性（例如自闭症谱系障碍和注意力缺陷/多动障碍）儿童的培训评估，专注于无障碍的低视力放大镜，以及无人驾驶飞机飞行员操作员的性能辅助。综合研究和教育目标是提供一个基础，指导、培训和吸引代表性不足的少数民族和女性学生从当地高中进入大学，并将他们过渡到STEM跨学科领域的研究生学习。这些拓展工作将为一群参加邻近学区夏令营的学生提供直接培训，目的是吸引代表性不足的高中生进入STEM领域。该项目还将通过一年一度的活动，提供使用该项目内置的眼动追踪技术的动手活动，对神经多样性儿童及其护理人员产生积极影响。本研究将通过眼动追踪流分析框架确定注意力和凝视转换的神经认知指数的相关性。该项目建立在使用分析流和多用户眼动追踪的聚合操作方法的基础上，并在与联合注意力和凝视转换相关的任务中建立了新的眼动追踪模型。通过创建一个“流媒体中心”，该项目工作将从根本上推进眼动追踪数据处理和分析的研究。接下来，研究在注意控制任务中收集的表征凝视转移的眼动指标之间的可能关系，并建立凝视转移模型。随后，研究将集中在工作记忆模型上，通过回答研究问题：“眼动追踪测量能否在复杂跨任务中反映工作记忆容量？”此外，考虑到大多数现有的眼动追踪系统只支持眼动数据的标准分析和离线批处理，该项目将推进计算效率高的流处理和实时分析，对广泛的眼动追踪应用有用。实时眼动追踪分析的进步将带来全新的应用领域。为此，研究团队将与不同的组织合作，如灯塔协会（非营利组织）和美国国家航空航天局（美国国家航空航天局），以及医疗保健/医疗专业人员。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The primary goal of this project is to use real-time processing of eye tracking data to derive non-invasive measurements that can reveal objective and quantifiable information about the attention of individual subjects (e.g., being able to select useful information). Research advances made through this project will be used towards improved training assessment for neurodiverse (e.g., Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder) children, low-vision magnifiers focusing on accessibility, and performance assistance for unmanned aircraft pilot operators. The integrated research and education goal is to provide a foundation to mentor, train, and attract underrepresented minority and female students from local high schools to a college pathway and transition them towards graduate studies on interdisciplinary areas in STEM. The outreach efforts will provide direct training to a cohort of students involved in a summer camp for neighboring school districts, with the aim of attracting underrepresented high school students to STEM fields. The project work will also positively impact neurodiverse children and their caregivers through an annual event that provides hands-on activities using the eye tracking technology built in this project.This research will determine the correlation of neurocognitive indices of attention and gaze transition via an eye tracking streaming analytics framework. The project builds on a converging operations approach using analytic-streams and multi-user eye tracking, and establishes novel eye tracking models during tasks associated with joint attention and gaze transition. The project work will fundamentally advance research in eye tracking data processing and analytics, by creating a “streaming hub”. Next, it will investigate the possible relationships between eye movement metrics representing gaze transition collected during attentional control tasks and build a gaze transition model. Subsequently, the research will focus on working memory models by answering the research question: “Can eye tracking measures index working memory capacity during complex span tasks?” Furthermore, considering that most existing eye tracking systems only support standard analysis and offline batch-processing of eye movement data, this project will advance computationally efficient stream processing and real-time analytics useful for a broad range of eye tracking applications. Advances in real-time eye tracking analytics will enable entirely new application areas. For this, the research team will collaborate with different organizations such as Lighthouse Guild (non-profit) and NASA (National Aeronautics and Space Administration), as well as healthcare/medical professionals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.