摘要： 在小型化、低功耗的可穿戴设备上,针对运行基于脑电信号的驾驶疲劳检测系统的准确率不高的问题,在对被试者左前额脑电信号Attention和Meditation以及Blink的数据进行关系分析的基础上,分别筛选最佳窗口宽度和分类算法,设计适用于可穿戴设备的疲劳驾驶检测算法,并在安卓智能设备上进行系统实现。采用准确率、正样本识别正确率、负样本识别正确率、敏感性与特异性指标,分别测试4种分类算法,即k临近算法、决策树算法、朴素贝叶斯算法、多层人工神经网络算法的性能,并最终选择kNN分类算法进行系统实现。实验结果证明,该系统的准确率达到83.7%,敏感性与特异性分别达到73.8%和88.6%,系统具有无线、实时、准确高效的特点。
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
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.
Key words:
wearable,
fatigue driving detection,
Electroencephalogram(EEG) signal,
blink frequency,
classification algorithm,
correlation coefficient
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