Translated Title
Wearable Fatigue Driving Detection System Based on Electroencephalogram and Blink Frequency
Translated Abstract
Aiming at the accuracy rate of fatigue driving detection system based on Electroencephalogram (EEG) signal running is not high on small size,low-powered wearable devices,on the basis of data relation analysis between Attention,Meditation and Blink of subject＇＇s left prefrontal brain electrical signal,the best window width and classification algorithm is selected.This paper designs fatigue driving detection algorithm suitable for wearable devices.And the system is implemented on the Android intelligent devices.The accuracy rate,true positives rate,false positives rate,sensitivity and specificity are used to measure the performance of four kinds of algorithm:k-nearest neighbors,decision tree,naive Bayes,multi-layer artificial neural network.kNN is chosen to implement system.Experimental results show that the accuracy rate of the system reaches 83.7％,sensitivity and specificity are 73.8％ and 88.6％.The system is wireless,real-time,accurate and efficient.
Translated Keyword
Electroencephalogram (EEG) signal
correlation coefficient
wearable
fatigue driving detection
blink frequency
classification algorithm
Access Number
WF:perioarticaljsjgc201702049
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