Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability
DOI: 10.3389/fncom.2024.1475530
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Summary
This systematic review investigates the relationship between driver cognitive impairment, mental workload (MWL), and physiological indicators, specifically heart rate variability (HRV) and eye-tracking metrics. The research is motivated by the critical need to enhance transportation safety by understanding how cognitive deficits, triggered by high mental workload, undermine a driver’s ability to process information and react to traffic scenarios. The study aims to synthesize existing literature to evaluate how physiological cues can serve as reliable proxies for assessing driver cognitive states, thereby informing the development of adaptive interfaces and safety interventions. The authors employed a rigorous methodology combining the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) frameworks. They sourced 120 articles from digital libraries including Web of Science, IEEE Xplore, and Elsevier, focusing on publications between 2014 and 2023. The review categorized studies into three main areas: adaptive interfaces and driver assistance systems, MWL assessment techniques, and the relationship between physiological measures and cognitive states. The analysis scrutinized various HRV metrics, including time-domain (SDNN, RMSSD), frequency-domain (LF, HF power), and non-linear metrics (Correlation Dimension, Sample Entropy), alongside eye-tracking data such as pupil diameter and gaze entropy. The findings indicate that HRV and infrared measurements are crucial for evaluating fatigue and workload in skilled drivers. Specifically, decreased HRV, particularly in non-linear metrics, correlates with increased mental tasks and cognitive load. Eye-tracking metrics, including pupil diameter changes and fixation duration, were identified as consistent indicators of cognitive load during multitasking and semi-autonomous driving scenarios. The review highlights that adaptive Human-Machine Interfaces (HMI) and eco-safe driving systems can promote safe behaviors without imposing excessive mental or visual workload. However, the authors note significant limitations in existing literature, including small sample sizes, a lack of diversity in participant demographics, and insufficient consideration of individual differences and real-world driving conditions. Many studies relied heavily on driving simulators, which may not fully capture the complexities of actual road environments. The significance of this review lies in its comprehensive synthesis of physiological markers for driver monitoring, providing a foundation for developing non-intrusive, real-time assessment tools. The authors conclude that while HRV and eye-tracking are promising, future research must incorporate additional indicators, such as subjective assessments and task performance metrics, to achieve a more holistic understanding of cognitive workload. Addressing current limitations, such as validating findings in real-world settings and accounting for individual variability, is essential for creating effective interventions that mitigate cognitive impairment and enhance overall road safety.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- workload measurement
- mental demand
- cognitive capacity variation
- stress driving
- drowsiness detection algorithms
- neuro workload indices
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: theory or model