Development of drowsy driving accident prediction by heart rate variability analysis

Drowsy driving accidents can be prevented if it can be predicted in advance. The present work aims to develop a new method for predicting a drowsy driving accident based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is the fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in process control. Driving simulator experiments demonstrated that driver drowsiness was successfully predicted seven out of eight cases before drowsy driving accidents occur.