Smart Driver Drowsiness Detection Model Based on Analytic Hierarchy Process

This paper proposes a smart driver drowsiness detection (SDDD) model for vehicles. The SDDD monitors a driver's heart rate variability (HRV) through electrocardiography (ECG) in real time to detect driver drowsiness. The SDDD processes the data of HRV and ECG to obtain a set of parameters with time-domain analysis, frequency-domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. In the process, a machine learning algorithm analyzes the parameters to detect driver drowsiness. The SDDD optimizes critical features with the analytic hierarchy process (AHP), which uses a feature extraction method through an iterative procedure. It is found that the SDDD in this study detects the level of driver drowsiness with higher sensitivity than previous models.