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

协作研究：动态现实环境中的认知工作负载分类：心磁图方法

• 批准号: 2320490
• 负责人: Asimina Kiourti
• 金额: $ 39.97万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320490&HistoricalAwards=false
• 关键词: Collaborative; Research; Cognitive; Workload; Classification

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.

认知工作负荷是指个人响应认知任务所付出的脑力劳动水平，不幸的是，目前还没有技术可以使用无缝、可靠和低功耗的方式来监控个人在现实环境中的认知工作负荷水平。我们建议通过使用佩戴在受试者胸部的新型心磁图（MCG）系统来填补这一空白，该系统允许传感器收集心脏自然发出的并与大脑活动相关的磁场。大大加快了此类方面的进展儿科脑震荡恢复、飞行员培训、改进的用户机器界面、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成果等多种学科除了基础科学方面的进步外，拟议的研究预计还将具有重大意义。通过针对跨学科教育和多样化招聘，我们打算通过研讨会和家庭友好型郊游向新受众展示 STEM 概念，并巧妙地将 MCG 传感器集成到网络物理系统 (CPS) 中。二相互连接的循环：（a）人机循环，解决不同认知状态阈值随时间的变化，以及（b）非人机循环，适应系统的我们的目标是：(1) 建立一个关于硬件/算法进步对现实环境中 MCG 传感器性能影响的知识库。这(3) 确保动态现实环境中的可操作性，并关闭网络和物理端之间的循环，以最大限度地减少资源使用。 (4) 在测量脑震荡儿童认知工作量的框架内验证 CPS。鉴于其巨大的临床潜力，选择这一人群：认知活动对儿童脑震荡恢复的影响目前尚不清楚，很大程度上是由于该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。