ArticleHealthcare technology letters2017
Developing and evaluating a mobile driver fatigue detection network based on electroencephalograph signals.
Article in Healthcare technology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Impact of fatigue levels on EEG-based personal recognition.Medical & biological engineering & computing · 2026Article
- Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study.Sensors (Basel, Switzerland) · 2021Article
- Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.Sensors (Basel, Switzerland) · 2020Review
- Design of electroencephalogram authentication access control to smart car.Healthcare technology letters · 2020Article
- Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model.Cognitive neurodynamics · 2018Article
- Real-time eye tracking for the assessment of driver fatigue.Healthcare technology letters · 2018Article
- Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel.Computational and mathematical methods in medicine · 2017Article
- Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.PloS one · 2017Article
- Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.Frontiers in computational neuroscience · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors' main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.
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Registered trials
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