Objective

This paper aimed to investigate the robustness of driver cognitive workload detection based on electrocardiogram (ECG) when considering temporal variation and individual differences in cognitive workload.

Background

Cognitive workload is a critical component to be monitored for error prevention in human–machine systems. It may fluctuate instantaneously over time even in the same tasks and differ across individuals.

Method

A driving simulation study was conducted to classify driver cognitive workload underlying four experimental conditions (baseline, N-back, texting, and N-back + texting distraction) in two repeated 1-hr blocks. Heart rate (HR) and heart rate variability (HRV) were compared among the experimental conditions and between the blocks. Random forests were built on HR and HRV to classify cognitive workload in different blocks and for different individuals.

Results

HR and HRV were significantly different between repeated blocks in the study, demonstrating the time-induced variation in cognitive workload. The performance of cognitive workload classification across blocks and across individuals was significantly improved after normalizing HR and HRV in each block by the corresponding baseline.

Conclusion

The temporal variation and individual differences in cognitive workload affects ECG-based cognitive workload detection. But normalization approaches relying on the choice of appropriate baselines help compensate for the effects of temporal variation and individual differences.

Application

The findings provide insight into the value and limitations of ECG-based driver cognitive workload monitoring during prolonged driving for individual drivers.

中文翻译：

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驾驶员认知工作量的时间变化和个体差异对基于 ECG 的检测的影响

客观的

本文旨在研究在考虑认知工作量的时间变化和个体差异时，基于心电图（ECG）的驾驶员认知工作量检测的鲁棒性。

背景

认知工作负载是人机系统中为预防错误而需要监控的关键组成部分。即使在相同的任务中，它也可能会随着时间的推移而瞬时波动，并且因人而异。

方法

进行了一项驾驶模拟研究，以在两个重复的 1 小时块中对四种实验条件（基线、N-back、发短信和 N-back + 发短信分心）下的驾驶员认知工作量进行分类。在实验条件之间和块之间比较心率（HR）和心率变异性（HRV）。随机森林建立在 HR 和 HRV 之上，用于对不同块和不同个体的认知工作量进行分类。

结果

HR 和 HRV 在研究中的重复块之间存在显着差异，表明时间诱导的认知工作量变化。通过相应的基线对每个块中的 HR 和 HRV 进行标准化后，跨块和跨个体的认知工作量分类的性能显着提高。

结论

认知工作量的时间变化和个体差异会影响基于 ECG 的认知工作量检测。但是依赖于选择适当基线的标准化方法有助于补偿时间变化和个体差异的影响。

应用

研究结果提供了深入了解基于 ECG 的驾驶员认知工作量监测在个体驾驶员长时间驾驶期间的价值和局限性。