Key result
A mobile driver fatigue detection network using a fuzzy entropy-based algorithm on EEG signals achieved an average classification accuracy of 95%.
A mobile driver fatigue detection network using EEG signals and fuzzy entropy achieved 95% accuracy in detecting fatigue states in a simulated driving experiment.
May support EEG-based fatigue alerts in drivers; leaves open real-world validation before clinical use.
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.
No takes yet. Share an insight, caveat, or question.
Yin et al. (2016) studied Driver fatigue (n=12). Fuzzy entropy-based EEG fatigue detection algorithm was evaluated on Accuracy of detecting driver fatigue. A mobile driver fatigue detection network using a fuzzy entropy-based algorithm on EEG signals achieved an average classification accuracy of 95%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: