Collaborative Research: Cognitive Workload Classification in Dynamic Real-World Environments: A MagnetoCardioGraphy Approach

认知工作量是指个体在应对认知任务时所付出的脑力劳动水平。不幸的是，目前还没有一种技术可以使用无缝、可靠和低成本的方法来监控个人在真实环境中的认知工作负荷水平。我们建议通过使用戴在受试者胸部的新型磁心图(MCG)系统来填补这一空白，以允许传感器收集心脏自然发出的与大脑活动相关的磁场。预计这项科学将极大地加快儿科脑震荡恢复、飞行员培训、改进用户-机器界面、建筑环境中的伤害预防、提高人类在危险任务中的表现以及改善教育结果等不同学科的进展。除了基础科学方面的进步，这项拟议的研究预计将引起学生和公众的极大兴趣。通过以跨学科教育和多样化招聘为目标，我们打算通过研讨会和适合家庭的郊游让新的受众接触STEM概念。建议的MCG传感器被巧妙地集成在具有两个相互连接的环路的网络物理系统(CPS)中：(A)人在环路中，其处理不同认知状态的阈值随时间的变化；以及(B)非人在环路，其适应系统的算法和硬件组件，以最小的资源使用对认知工作负荷进行高精度分类。我们的目标是：(1)建立一个关于硬件/算法进步对现实环境中心磁图传感器性能的影响的知识库。(2)从心电数据中探索认知负荷的分类，并与佩戴者闭合回路进行动态校准，以解决认知状态随时间变化的阈值。(3)确保在动态现实环境中的可操作性，并关闭网络和物理端之间的环路，以最大限度地减少资源消耗。(4)在测量脑震荡儿童认知负荷的框架内对CPS进行验证。在不丧失一般性的情况下，我们选择这一群体是因为其巨大的临床潜力：认知活动对儿童脑震荡恢复的影响目前尚不清楚，主要是由于量化认知活动工作量的困难。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Cognitive workload refers to the level of mental effort put forth by an individual in response to a cognitive task. Unfortunately, no technology currently exists that can monitor an individual’s levels of cognitive workload in real-world environments using a seamless, reliable, and low-cost approach. We propose to fill this gap by using a novel magnetocardiography (MCG) system worn upon the subject’s chest to allow the sensor to collect the magnetic fields that are naturally emanated by the heart and associated with brain activity. This science is anticipated to greatly accelerate progress in such diverse disciplines as pediatric concussion recovery, pilot training, improved user-machine interfaces, injury prevention in construction environments, increased human performance in risky missions, and improved education outcomes. In addition to advances in basic science, the proposed research is expected to be of significant interest to students and the public. Through targeting interdisciplinary education and diverse recruitment, we intend to expose new audiences to STEM concepts via workshops and family-friendly outings. The proposed MCG sensor is smartly integrated in a Cyber-Physical System (CPS) with two inter-connected loops: (a) a human-in-the-loop that addresses changes in the thresholds of different cognitive states as a function of time, and (b) a non-human-in-the-loop that adapts the system’s algorithmic and hardware components for high-accuracy classification of cognitive workload with minimum resource usage. Our goals are to: (1) Build a knowledgebase concerning the impact of hardware/algorithmic advances upon MCG sensor performance in real-world settings. (2) Explore the classification of cognitive workload from MCG data and close the loop with the wearer for dynamic calibrations that address the time-varying thresholds of cognitive states. (3) Ensure operability in dynamic real-world settings and close the loop between the cyber and physical sides for minimal resource usage. (4) Validate the CPS within the framework of measuring cognitive workload for children with concussion. Without loss of generality, we select this population given the immense clinical potential: the effects of cognitive activity on pediatric concussion recovery are currently unknown, largely due to the difficulties in quantifying cognitive activity workload.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.