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The dynamics of driving performance in non-optimal human states: Drowsiness, high cognitive load, and acute stress.

Traffic injury prevention·2026
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Heart rate dynamics for cognitive load estimation in a driving simulation task.

Karina Rollandovna Arutyunova1, Anastasiia Vladimirovna Bakhchina2, Daniil Igorevich Konovalov2

  • 1Harman International, HarmanX Neurosense, 30001 Cabot Dr, Novi, MI, 48377, USA. karina.arutyunova@harman.com.

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|December 31, 2024
PubMed
Summary

Heart rate and heart rate variability metrics accurately detect cognitive load (CL) and mental stress in drivers. These physiological signals offer a valid approach for monitoring driver states and enhancing road safety.

Keywords:
Cognitive loadDriving simulationHeart rateHeart rate variabilityMental stress

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Area of Science:

  • Psychology
  • Human-Computer Interaction
  • Physiology

Background:

  • Cognitive load (CL) significantly impacts driver performance and can lead to mental stress.
  • Heart rate (HR) and heart rate variability (HRV) are known physiological indicators of cognitive states.
  • Existing research suggests HR and HRV can reflect CL, but their accuracy in driving contexts needs further exploration.

Purpose of the Study:

  • To investigate the accuracy of HR and HRV metrics in differentiating varying cognitive load conditions during simulated driving.
  • To assess the potential of these physiological measures for real-time monitoring of driver mental stress.

Main Methods:

  • A large-scale study involving 892 participants performing simulated driving tasks (highway and urban).
  • Inclusion of an n-back task to systematically increase cognitive load.
  • Analysis of heart rate (HR) and heart rate variability (HRV) metrics, including RMSSD and permutation entropy.

Main Results:

  • Increased cognitive load correlated with higher HR, reduced HRV (RMSSD), and increased HR complexity (permutation entropy).
  • HR demonstrated the highest accuracy in distinguishing between different CL levels, especially in differentiating highway vs. urban driving and during mental distraction.
  • Gender and age influenced the discriminative accuracy of HR and HRV metrics, aligning with subjective CL ratings.

Conclusions:

  • HR and HRV indices are valid physiological markers for monitoring cognitive load and detecting mental stress in drivers.
  • These findings support the development of applications for real-time driver state assessment and safety enhancement.
  • The study highlights the potential of non-invasive physiological monitoring in intelligent transportation systems.