Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea.
Sensors (Basel). 2013 Dec 2;13(12):16494-511. doi: 10.3390/s131216494.
Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.
疲劳驾驶与酒驾同样危险，可能导致车祸。心率变异性 (HRV) 分析最近已被用于检测驾驶员困倦。然而，由于驾驶员的 HRV 信号一直被视为静态信号，因此检测可靠性低于预期。小波变换方法是一种用于分析非平稳信号的方法。本研究旨在使用 HRV 信号的小波变换对短时间内的警觉和困倦驾驶事件进行分类，并将该方法的分类性能与使用快速傅里叶变换 (FFT) 特征的传统方法进行比较。基于基于 FFT 的短期 HRV 评估的最短持续时间标准，对 2 分钟的 HRV 样本进行小波分解，同时也对 1 分钟和 3 分钟的样本进行参考。使用接收器操作曲线 (ROC) 分析和支持向量机 (SVM) 分类器分别进行特征选择和分类。ROC 分析结果表明，无论使用的 HRV 样本持续时间如何，基于小波的方法都比基于 FFT 的方法表现更好。最后，根据驾驶员困倦检测的实时要求，使用从四个受试者的 1 分钟 HRV 信号中提取的八十个基于 FFT 和基于小波的特征来训练 SVM 分类器。基于小波的特征的平均留一 (LOO) 分类性能为 95%的准确率、95%的灵敏度和 95%的特异性。这优于基于 FFT 的结果，其准确率为 68.8%，灵敏度为 62.5%，特异性为 75%。此外，所提出的硬件平台价格低廉，易于使用。
