Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

• Published in: Sensors (Basel, Switzerland) Vol. 13; no. 12; pp. 16494 - 16511
• Main Authors: Li, Gang, Chung, Wan-Young
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.12.2013; Molecular Diversity Preservation International (MDPI)
• Subjects: Accuracy; Automobile Driving; Bluetooth; Classification; driver fatigue; Drunk driving; Electrocardiography; Fatigue; Fourier transforms; Heart rate; Heart Rate - physiology; heart rate variability; Humans; internet; Methods; Nervous system; photoplethysmography; Physiology; Power; Sensors; Sleep Stages - physiology; smartphone; Support Vector Machine; Support vector machines; Time series; Wavelet Analysis; wavelet decomposition; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• 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.