计算机工程2017，Vol.43Issue(2)：293-298,303,7.DOI:10.3969/j.issn.1000-3428.2017.02.049
基于脑电与眨眼频率的可穿戴疲劳驾驶检测系统
Wearable Fatigue Driving Detection System Based on Electroencephalogram and Blink Frequency
摘要
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.关键词
可穿戴/疲劳驾驶检测/脑电信号/眨眼频率/分类算法/相关系数Key words
wearable/fatigue driving detection/Electroencephalogram (EEG) signal/blink frequency/classification algorithm/correlation coefficient分类
信息技术与安全科学引用本文复制引用
张丞,何坚,张岩,周明我..基于脑电与眨眼频率的可穿戴疲劳驾驶检测系统[J].计算机工程,2017,43(2):293-298,303,7.基金项目
国家自然科学基金(61040039,61201361) （61040039,61201361）
北京市自然科学基金(4102005,4122010). （4102005,4122010）
