ArticleSensors (Basel, Switzerland)2017
A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.PloS one · 2020Pooled it
- Domain-robust vision transformer with hierarchical swin encoding for explainable low-latency driver drowsiness detection.Scientific reports · 2026Article
- A Study on the Correlation Between Driving Behavior and ECG Data in Driving Fatigue.Sensors (Basel, Switzerland) · 2026Article
- Divergent eye-brain-heart fatigue signatures during sustained simulated agricultural operations.Frontiers in physiology · 2026Article
- To assure aviation safety: the pilot fatigue detection based on short-term multimodal physiological signals.Frontiers in human neuroscience · 2026Article
- Real-time monitoring and prediction of remote operator fatigue in plateau deep mining based on dynamic Bayesian networks.Scientific reports · 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
- Capacitance-Based Untethered Fatigue Driving Recognition Under Various Light Conditions.Sensors (Basel, Switzerland) · 2024Article
- Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.Sensors (Basel, Switzerland) · 2024Article
- Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition.Scientific reports · 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
- Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance.Sensors (Basel, Switzerland) · 2023Article
- Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips.Sensors (Basel, Switzerland) · 2023Article
- Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.Frontiers in physiology · 2023Article
- Decoding the cognitive states of attention and distraction in a real-life setting using EEG.Scientific reports · 2022Article
- Drowsiness Detection Using Ocular Indices from EEG Signal.Sensors (Basel, Switzerland) · 2022Article
- A Review of Recent Developments in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2022Review
- Non-Intrusive Contact Respiratory Sensor for Vehicles.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired
Indexed as
Identifiers
What OpenQuestion holds
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.