ArticleMedical & biological engineering & computing2016
Drowsiness detection using heart rate variability.
Article in Medical & biological engineering & computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- On the potential of transauricular electrical stimulation to reduce visually induced motion sickness.Scientific reports · 2023Trial
- Tracking Vigilance While Driving: Pilot Study of Heart Rate Variability Classification Under a Controlled Sleep-Deprivation Protocol.Sensors (Basel, Switzerland) · 2026Article
- A multimodal drowsiness dataset using video, biometric, and behavioral data.Scientific data · 2026Article
- Early Drowsiness Detection via Second-Order Derivative Analysis of Heart Rate Variability: A Non-Contact ECG Approach with Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Including Eye Movement in the Assessment of Physical Fatigue Under Different Loading Types and Road Slopes.Journal of eye movement research · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- Effects of constant and incremental modes of music volume and odor concentration on vigilance.Frontiers in psychology · 2026Article
- Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.Scientific reports · 2025Article
- DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data.Sensors (Basel, Switzerland) · 2025Article
- Beyond the time-on-task: an EEG-driven approach for effective physiological assessment of mental fatigue in simulated and real driving.Frontiers in bioengineering and biotechnology · 2025Article
- The impact of working night shifts on cardiac autonomic nervous regulation during the six-minute walk test in nurses.BMC nursing · 2024Article
- Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.Behavioral sciences (Basel, Switzerland) · 2024Article
- Detection of human drowsiness by the vestibulo-ocular reflex compensating for heartbeat-induced head perturbations.Scientific reports · 2024Article
- Sleep deprivation alters utilization of negative feedback in risky decision-making.Frontiers in psychiatry · 2024Article
- Sixty years in service to international biomedical engineering community.Medical & biological engineering & computing · 2023Article
- A systematic review of physiological signals based driver drowsiness detection systems.Cognitive neurodynamics · 2023Review
- Home-Based Dynamics of Sleepiness-Related Conditions Starting at Biological Evening and Later (Beyond Working).International journal of environmental research and public health · 2023Article
- Detecting Soldiers' Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities.Military medicine · 2022Review
- Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review.Sensors (Basel, Switzerland) · 2022Review
- Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is estimated that 10-30 % of road fatalities are related to drowsy driving. Driver's drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous nervous system activity, which can be measured noninvasively from the heart rate variability (HRV) signal obtained from surface electrocardiogram, presents alterations during stress, extreme fatigue and drowsiness episodes. We hypothesized that these alterations manifest on HRV and thus could be used to detect driver's drowsiness. We analyzed three driving databases in which drivers presented different sleep-deprivation levels, and in which each driving minute was annotated as drowsy or awake. We developed two different drowsiness detectors based on HRV. While the drowsiness episodes detector assessed each minute of driving as "awake" or "drowsy" with seven HRV derived features (positive predictive value 0.96, sensitivity 0.59, specificity 0.98 on 3475 min of driving), the sleep-deprivation detector discerned if a driver was suitable for driving or not, at driving onset, as function of his sleep-deprivation state. Sleep-deprivation state was estimated from the first three minutes of driving using only one HRV feature (positive predictive value 0.80, sensitivity 0.62, specificity 0.88 on 30 drivers). Incorporating drowsiness assessment based on HRV signal may add significant improvements to existing car safety systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.