Average heart rate for driver monitoring systems
DOI: 10.1504/ijhfe.2016.083521
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Summary
This study investigates whether average heart rate serves as a sensitive and reliable metric for monitoring driver mental workload, particularly in the context of semi-autonomous vehicles requiring transitions of control. Current driver monitoring systems primarily rely on steering behavior and ocular parameters, which may be insufficient when drivers are not actively steering or looking at the road. The authors aim to determine if physiological measures, specifically heart rate, can accurately detect varying levels of cognitive demand and how these measures are influenced by driver age and gender. The research involved 136 volunteers (72 females) aged 21 to 70, categorized into three age groups: 21–34, 35–53, and 54–70 years. Participants drove ten different 2015 model year vehicles equipped with various in-vehicle infotainment systems (IVIS). Data were collected during two sessions: an initial session and a follow-up session after a week of practice using the IVIS. Participants performed three tasks while driving: a single-task condition (driving only), an IVIS interaction condition involving voice commands for phone and radio functions, and an Operational Span (OSPAN) task, a highly demanding cognitive exercise involving memory and math verification. Heart rate was monitored using Zephyr BioHarness 3 devices, and average beats per minute were calculated for each condition. The results demonstrated a significant main effect of task on heart rate, with average heart rate increasing as task demand increased. The OSPAN condition yielded the highest heart rate (76.53 bpm), followed by IVIS interaction (74.54 bpm), and the single-task condition (73.89 bpm). A significant interaction between task and age was observed, indicating that younger drivers (21–34 years) exhibited a steeper increase in heart rate as tasks became more demanding compared to older drivers. Specifically, the slope of heart rate increase was highest for the youngest group (1.78) and lowest for the oldest group (0.88). No significant main effects were found for gender or session, suggesting that practice did not significantly alter the physiological workload response measured by average heart rate. These findings suggest that average heart rate is a viable metric for detecting mental workload in driving scenarios. However, the significant age-related differences imply that driver monitoring systems must incorporate age-specific thresholds to accurately interpret physiological data. As heart rate naturally decreases with age, algorithms relying on fixed heart rate benchmarks may misclassify the workload state of older drivers. The study highlights the importance of adaptive monitoring systems that account for demographic variables to ensure safety during automated-to-manual control transitions.
Key finding
Average heart rate increased as secondary tasks became more demanding, with younger drivers showing increased heart rate compared to older drivers under high workload, supporting heart rate as a driver monitoring metric.
Methodology
on_road
Sample size: 136
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. Discovered via tag_papers on 2026-05-30 (4 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-06 |
| archive | failed | pmc | — | — | 12 | 2026-06-04 |
| extract | success | cached | — | — | 5 | 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 |
| enrich | success | openalex | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 2 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 4 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 28 | 2026-08-11 |
| verify | success | — | — | — | 4 | 2026-08-11 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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Information type
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- Empirical Findings: physiological data, behavioral performance data, observational prevalence