EAGER: Preliminary Studies for Real-Time Tracking of Cognitive Fatigue via Eye Movement Patterns

EAGER：通过眼动模式实时跟踪认知疲劳的初步研究

• 批准号: 1551850
• 负责人: Deepak Ganesan
• 金额: $ 15万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551850&HistoricalAwards=false
• 关键词: EAGER; Preliminary; Studies; Real; Time

This project seeks to perform a preliminary exploration into the design of an end-to-end mHealth system for detecting the fatigue state among individuals by leveraging eye movements. Real-time detection of fatigue state is important for a wide range of applications including reducing medical errors, reducing motor vehicle operator errors, and improving overall health and wellness for the general public. Despite these benefits, there is no widely available method for predicting fatigue in the natural environment.In this project, we are designing a real-time analytics engine that provides general computational methods for inferring fatigue by fusing raw time-series data about various eye measures (saccades, fixations, blinks, and pupil dilation). This work includes a novel fatigue detector for natural settings that address robustness to real-world variability, individual differences, and confounders, and develops time-series pattern extraction methods to characterize temporal progression of fatigue state. We are also performing a pilot evaluation of our design for a student population. We envisage that a successful pilot will lead to more extensive studies involving medical residents, motor vehicle operators, shift workers, and other user groups where fatigue is a significant safety or health issue. This project includes outreach including a two day mobile sensing-themed workshop for middle school students. The workshop is offered at the UMass Amherst campus and builds on an existing outreach partnership with Girls Inc. of Holyoke, MA. that is aimed at addressing the widely recognized under-representation of women in STEM fields.

该项目旨在对端到端MHealth系统的设计进行初步探索，以通过利用眼球运动来检测个体之间的疲劳状态。对疲劳状态的实时检测对于广泛的应用很重要，包括减少医疗错误，减少机动车操作员错误以及改善普通公众的整体健康和健康。尽管有这些好处，但在自然环境中尚无广泛可用的方法来预测疲劳。在这个项目中，我们正在设计一种实时分析引擎，该引擎通过融合有关各种眼睛测量的原始时间序列数据（扫视，固定，固定，眨眼，眨眼和学生扩张）提供了一般的计算方法来推断疲劳。这项工作包括一种用于自然设置的新型疲劳探测器，该检测器可解决对现实世界的可变性，个体差异和混杂因素的鲁棒性，并开发时间序列模式提取方法以表征疲劳状态的时间进展。我们还在对学生人数的设计进行试点评估。我们设想，成功的飞行员将导致更广泛的研究，涉及医疗居民，机动车运营商，班次工人以及其他疲劳是一个重大安全或健康问题的用户群体。该项目包括外展活动，包括为期两天的中学学生移动感测为主题的研讨会。该研讨会是在UMass Amherst校园提供的，并建立在与马萨诸塞州Holyoke的Girls Inc.的现有外展伙伴关系的基础上。这目的是解决在STEM领域中众所周知的妇女代表性不足的问题。