ArticleSensors (Basel, Switzerland)2017
Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions.
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 28 papers.
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Who cites it
28 citing papers in PubMed.
- Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.NeuroSci · 2026Article
- A fatigue driving detection method based on driver posture and facial state analysis.Scientific reports · 2026Article
- LDIE-FDNet: Lightweight dynamic image enhancement-enabled real-time fatigue driving detection network.PloS one · 2026Article
- Research on Driver Fatigue Detection in Real Driving Environments Based on Semi-Dry Electrodes with Automatic Conductive Fluid Replenishment.Sensors (Basel, Switzerland) · 2025Article
- Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.Scientific reports · 2025Article
- A Comprehensive Review of Unobtrusive Biosensing in Intelligent Vehicles: Sensors, Algorithms, and Integration Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection.Biomimetics (Basel, Switzerland) · 2025Article
- Integrating DRN-RF with computer vision for detection of control room operator's mental fatigue.PloS one · 2025Article
- Review
- Efficient detection of driver fatigue state based on all-weather illumination scenarios.Scientific reports · 2024Article
- Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection.Frontiers in neuroscience · 2023Article
- Comparison of Eye and Face Features on Drowsiness Analysis.Sensors (Basel, Switzerland) · 2022Article
- A Review of Recent Developments in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2022Review
- Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces.Frontiers in neuroergonomics · 2022Review
- Assessment of Combination of Automated Pupillometry and Heart Rate Variability to Detect Driving Fatigue.Frontiers in public health · 2022Article
- EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization.Sensors (Basel, Switzerland) · 2021Article
- REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health.International journal of environmental research and public health · 2021Review
- Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data.Sensors (Basel, Switzerland) · 2021Article
- An Evaluation of Executive Control Function and Its Relationship with Driving Performance.Sensors (Basel, Switzerland) · 2021Article
- A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification.Brain sciences · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: "wake" and "drowsy". The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the "awake" state, and 15.15% false detections of the "drowsy" state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.
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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.