Real-Time Driver Drowsiness Detection Using Wavelet Transform and Ensemble Logistic Regression

Drowsy-driver-related accidents has increased in recent years. Research and systems development aim to reduce traffic-accident-related injuries and fatalities. These potentially life-saving systems must operate in a timely manner with the highest precision. In the past two decades, researchers proposed method based on driving pattern changes, driver body position, and physiological signal processing patterns. There is a focus on human physiological signals, specifically the electrical signals from the heart and brain. In this paper, we are presenting an alternative method to determine and quantify driver drowsiness levels using a physiological signal that was collected in a non-intrusive method. This methodology utilizes heart rate variation (HRV), electrocardiogram (ECG), and machine learning for drowsiness detection. Thirty subjects were recruited and ECG data was collected as each subject drifted off to sleep and while sleeping for a duration of between four and eight hours of normal sleep. After using the continuous wavelet transform for the feature extraction, a new feature selection was executed using ensemble logistic regression (ELR), which achieved an average accuracy of 92.5% using data acquired from thirty subjects in an average of 21s. Successful application of this drowsiness detection method may help prevent traffic accidents.