Supervised Machine Learning-Assisted Driving Stress Monitoring MIMO Radar System

Factors such as road traffic, challenging ambient temperature conditions, and extended periods of driving have detrimental effects on the physical and mental well-being of a driver. These factors can alter the stress levels, thereby diminishing his or her capacity to make effective decisions when faced with hazardous situations on the road. In this regard, this study presents a novel approach utilizing a multiple-input multiple-output (MIMO) radar system to accurately assess driver stress levels by measuring both physiological signals and driving behavior. The proposed method, assisted by a machine learning technique, provides comprehensive insights to classify the stress level of a driver into three states: drowsiness, awakeness, and anxiety. The MIMO radar system captures the elongation distance and velocity of six specific regions of the frontal torso of the driver in an advanced driving simulator based on virtual reality. This allows for the extraction of vital physiological parameters such as heart rate, respiratory rhythm, and breathing patterns over time, as well as the identification of changes in driving style determined by variations in their relative position in the seat and control of the steering wheel. Then, a fully connected neural network (FCNN) model is trained with the acquired data, and its performance is evaluated with volunteers submitted to different driving situations that induce stress in the driver. The findings show an accuracy in drowsiness detection of
${90}\%$
, awakeness of
${96}\%$
and anxiety of
${85}\%$
.