Robust and efficient driver monitoring system using a synthesis of facial features and biosignals

Abstract

Monitoring the driver's state of consciousness and fatigue is exclusively important to reduce the number of traffic accidents. We have proposed a collective data fusion method for checking driver safety levels by combining eye features and Heart rate Variability (HRV). Fatigue behavior was determined via facial image processing using Matlab as a tool. After detecting the face, the location of the eyes will be detected using the duration of eye closure, and the frequency of eye blinks. It is used as a distinctive characteristic to judge whether a driver is drowsy or not. General duration of closure is 0.15 to 0.25 seconds. Initially the HRV analysis was accomplished by acquiring Electrocardiogram (ECG) signal through non-intrusive ECG sensors wrapped on to the steering wheel followed by filtering noises and we have calculated pulse rate via peak detection using LabVIEW. If there are any abnormal signs found in the ECG of the driving person then the patient is wide-open to have a second order attack. The corresponding heart rate is sent as an alert SMS to the medical practitioner by microcontroller through GSM modem.