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.