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

摘要

Drowsy-driver-related accidents has increased in recent years. Research and systems development aim to reduce traffic-accidentrelatedinjuries and fatalities. These potentially life-saving systems must operate in a timely manner with the highest precision. Inthe past two decades, researchers proposed method based on driving pattern changes, driver body position, and physiologicalsignal processing patterns. There is a focus on human physiological signals, specifically the electrical signals from the heart andbrain. In this paper, we are presenting an alternative method to determine and quantify driver drowsiness levels using a physiologicalsignal 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 aseach subject drifted off to sleep and while sleeping for a duration of between four and eight hours of normal sleep. After using thecontinuous 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 21 s. Successfulapplication of this drowsiness detection method may help prevent traffic accidents.