Physiological markers of vigilance variation in a supervisory task

Maille, Nicolas; Salomone, Mick; Desantis, Andrea; Le Goff, Kevin; Sciabica, Jean-François; Bressolle, Marie-Christine; Berberian, Bruno · 2022 · Crossref

DOI: 10.54941/ahfe1001819

archive: archived pipeline: cataloged verified

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Summary

This study investigates the efficacy of physiological markers in assessing vigilance variation, specifically aiming to complement traditional subjective and performance-based methods in ecological settings like cockpit simulations. The authors address the limitation that performance metrics often fail to detect vigilance decrements in supervisory tasks, where alertness levels can fluctuate without immediate impact on task output. To identify robust physiological indicators, the researchers conducted two experiments using a Psychomotor Vigilance Task (PVT) and a novel Simplified Aircraft Monitoring Task (SAMT), which combined autopilot supervision with alarm detection. Seventeen participants were divided into two groups: eight completed the PVT, and nine completed the SAMT. The study measured four physiological markers: heart rate variability (HRV, specifically SDNN) via electrocardiography, eye closure percentage (PERCLOS) and blink frequency via oculometry, and alpha power via electroencephalography. The PVT involved reacting to visual stimuli over 30 minutes, while the SAMT required monitoring aircraft parameters and detecting alarms across blocks of varying frequency. In the PVT, behavioral reaction times did not show a significant decrease over time. However, physiological data revealed that HRV and PERCLOS increased linearly with time on task, and both significantly improved the prediction of reaction times compared to models using reaction times alone. Alpha power also showed a positive correlation with reaction times, indicating higher alpha activity during slower responses. Blink frequency showed no significant relationship with time or performance. In the SAMT, HRV and PERCLOS again demonstrated significant increases over time, mirroring the vigilance decrement pattern. Alpha power showed a marginal increase. Subjective fatigue ratings also increased significantly after the SAMT. The findings confirm that HRV, PERCLOS, and EEG alpha power are sensitive markers of vigilance decrement, outperforming blink frequency. Crucially, these physiological metrics detected vigilance changes even when behavioral performance metrics did not, particularly in the PVT. The consistency of these markers across both the simple PVT and the more complex, ecologically valid SAMT suggests their robustness. The study concludes that combining ECG and eye-tracking indicators offers a promising, unobtrusive solution for monitoring pilot vigilance in future cockpit environments, paving the way for further validation in representative simulator contexts.

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Topics

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• sustained attention vigilance
• workload measurement
• neuro workload indices
• vigilance
• drowsiness detection algorithms
• signal detection theory

Information type

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• Empirical Findings: physiological data, behavioral performance data