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.

本项目旨在对端到端移动医疗系统的设计进行初步探索，该系统通过利用眼球运动来检测个人的疲劳状态。疲劳状态的实时检测对于广泛的应用非常重要，包括减少医疗错误，减少机动车辆操作员的错误，以及改善公众的整体健康和健康。尽管有这些好处，目前还没有广泛可用的方法来预测自然环境中的疲劳。在这个项目中，我们正在设计一个实时分析引擎，通过融合各种眼睛测量（扫视、注视、眨眼和瞳孔扩张）的原始时间序列数据，为推断疲劳提供通用的计算方法。这项工作包括一种用于自然环境的新型疲劳检测器，该检测器解决了对现实世界变异性、个体差异和混杂因素的鲁棒性，并开发了时间序列模式提取方法来表征疲劳状态的时间进展。我们也在对我们的学生群体设计进行试点评估。我们设想，成功的试点将导致更广泛的研究，涉及住院医生、机动车辆操作员、轮班工人和其他疲劳是重大安全或健康问题的用户群体。这个项目包括一个面向中学生的为期两天的移动传感主题研讨会。该研讨会在马萨诸塞大学阿默斯特校区举办，并建立在与马萨诸塞州霍利奥克女孩公司现有的外展合作伙伴关系的基础上。旨在解决人们普遍认为女性在STEM领域代表性不足的问题。