Houshmand Sara, Kazemi Reza, Salmanzadeh Hamed
Department of Mechanical Engineering, KN. Toosi University of Technology, Tehran, Iran.
Department of Industrial Engineering, KN. Toosi University of Technology, Tehran, Iran.
J Med Signals Sens. 2022 Nov 10;12(4):294-305. doi: 10.4103/jmss.jmss_124_21. eCollection 2022 Oct-Dec.
Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data.
A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data.
According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier.
According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.
疲劳驾驶是全球严重交通事故的主要原因之一。在本研究中，提出了一种基于疲劳程度的分析方法，通过脑电图（EEG）测量和车辆动力学数据来检测疲劳。
使用驾驶模拟器收集清醒和疲劳状态下的大脑数据。对19名健康男性进行了测试。记录了来自顶叶、枕叶和中央部分的脑信号。使用疲劳观察者评分（ORD）进行疲劳阶段评估。本研究采用了一种创新方法，即相对于时间而言，根据ORD分析疲劳EEG数据。提取了EEG信号的13个特征，然后通过邻域成分分析（一种特征选择方法），选择了包括均值、标准差、峰度、能量和熵在内的5个特征。采用了六种分类方法，包括K近邻（KNN）、回归树、分类树、朴素贝叶斯、支持向量机回归和集成回归。此外，将横向位置和转向角作为车辆动力学数据用于检测疲劳，并将结果与基于EEG数据的分类结果进行比较。
根据EEG数据的分类结果，分类树和集成回归分类器分别检测到中度疲劳水平下超过87.55%和87.48%的疲劳情况。此外，分类结果表明，如果仅使用单通道P4，其性能要高于使用所有通道（C3、C4、P3、P4、O1、O2）的数据。分类树分类器和回归分类器使用单通道P4数据时的性能分别为91.31%和91.12%。基于车辆动力学数据的最佳分类结果通过KNN分类器为75.11。
根据本研究，可以基于从单通道P4数据中提取的特征检测出中度疲劳水平的驾驶员疲劳情况。
