Detecting driver fatigue using heart rate variability: A systematic review
DOI: 10.1016/j.aap.2022.106830
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Summary
This systematic review investigates the efficacy of heart rate variability (HRV) as a physiological marker for detecting driver fatigue, addressing the limitations of conventional monitoring systems. Traditional methods relying on driving performance metrics (e.g., steering, speed) and facial features (e.g., eye closure) are increasingly challenged by the adoption of automated driving systems, which reduce driver engagement and render these behavioral indicators unreliable. The authors posit that HRV, which reflects autonomic nervous system activity, offers a viable alternative that can be measured unobtrusively in real-life driving scenarios. The study aims to summarize existing literature on how HRV features change under fatigue, evaluate the performance of HRV-based detection systems, and assess the potential for real-world deployment. The authors conducted a systematic search of PubMed, Scopus, and Web of Science, identifying 977 records. After rigorous screening based on eligibility criteria—including the requirement for original research focusing exclusively on HRV and car driving tasks—18 articles were selected for review. The analysis extracted data on study demographics, driving tasks (simulator vs. on-road), fatigue induction methods (e.g., sleep deprivation, circadian timing, monotonous driving), measurement technologies (ECG, PPG, microphone sensors), and reference fatigue measures (subjective ratings like the Karolinska Sleepiness Scale, observer ratings, or EEG). The review also examined the specific HRV features analyzed and the validation methods used in studies that developed fatigue classifiers. The findings reveal significant inconsistency across the reviewed studies. While reduced heart rate was generally observed in fatigued drivers, changes in other time and frequency domain HRV features were not uniform. Among the 11 studies that developed HRV-based fatigue detection systems, detection accuracy varied widely, ranging from 44% to 100%. This variability is attributed to differences in study designs, including sample sizes (ranging from 2 to 86 participants), age and sex demographics, driving conditions, and the methods used to define and measure fatigue. The review highlights that demographic factors, particularly age and sex, significantly influence HRV indices, complicating the development of universal detection models. Furthermore, while wearable sensors like PPG offer greater usability than traditional ECG electrodes, they may suffer from reduced detection performance due to motion artifacts. The authors conclude that while HRV holds promise for driver fatigue detection, current evidence is insufficient for widespread deployment. The lack of consensus on HRV changes during fatigue and the high variability in detection performance underscore the need for standardized study designs. Future research must thoroughly test HRV-based systems in real-life conditions with diverse participants and comprehensive driving scenarios. Establishing a clearer understanding of the relationship between HRV, specific fatigue causal factors, and driver performance is essential to develop robust, reliable monitoring systems that can enhance road safety in the era of automated driving.
Key finding
Heart rate variability shows potential for detecting driver fatigue, but current detection systems exhibit highly variable accuracy ranging from 44% to 100% due to inconsistent study designs and lack of consensus on HRV changes during fatigue.
Methodology
review
Sample size: 18
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 | 2026-06-05 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-06 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
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Information type
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics, tool software