Kaiss Ali, Yang Jingzhen, Kiourti Asimina
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA.
Center for Research Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA.
Sensors (Basel). 2025 Aug 5;25(15):4806. doi: 10.3390/s25154806.
Despite cognitive workload (CW) being a critical metric in several applications, no technology exists to seamlessly and reliably quantify CW. Previously, we demonstrated the feasibility of a wearable MagnetoCardioGraphy (MCG) sensor to classify high vs. low CW based on MCG-derived heart rate variability (mHRV). However, our sensor was unable to address certain critical operational requirements, resulting in noisy signals, often to the point of being unusable. In addition, test conditions for the participants were not decoupled from motion (i.e., physical activity (PA)), raising questions as to whether the noted changes in mHRV were attributed to CW, PA, or both. This study reports software and hardware advancements to optimize the MCG data quality, and investigates whether changes in CW (in the absence of PA) can be reliably detected. Performance is validated for healthy adults (n = 10) performing three types of CW tasks (one for low CW and two for high CW to eliminate the memory effect). Results demonstrate the ability to retrieve MCG R-peaks throughout the recordings, as well as the ability to differentiate high vs. low CW in all cases, confirming that CW does modulate the mHRV. A paired Bonferroni t-test with significance α=0.01 confirms the hypothesis that an increase in CW decreases mHRV. Our findings lay the groundwork toward a seamless, practical, and low-cost sensor for monitoring CW.
尽管认知工作量（CW）在多个应用中是一个关键指标，但目前还没有技术能够无缝且可靠地量化CW。此前，我们证明了一种可穿戴式心磁图（MCG）传感器基于MCG衍生的心率变异性（mHRV）对高与低CW进行分类的可行性。然而，我们的传感器无法满足某些关键的操作要求，导致信号嘈杂，甚至常常无法使用。此外，参与者的测试条件并未与运动（即身体活动（PA））分离，这就引发了一个问题，即所观察到的mHRV变化是归因于CW、PA还是两者皆有。本研究报告了软件和硬件方面的进展，以优化MCG数据质量，并研究在没有PA的情况下是否能够可靠地检测到CW的变化。对10名健康成年人执行三种类型的CW任务（一种低CW任务和两种高CW任务以消除记忆效应）的性能进行了验证。结果表明在整个记录过程中能够检索到MCG R波峰，以及在所有情况下区分高与低CW的能力，证实了CW确实会调节mHRV。显著性α = 0.01的配对Bonferroni t检验证实了CW增加会降低mHRV这一假设。我们的研究结果为开发一种用于监测CW的无缝、实用且低成本的传感器奠定了基础。
