Non-contact Monitoring of Fatigue Driving Using FMCW Millimeter Wave Radar

Fatigue driving is the leading cause of severe traffic accidents, which is considered as an important point of the research. Although a precise definition of fatigue is lacking, it is possible to detect the physiological characteristics of the human body to determine whether a person is fatigued, such as head shaking, yawning, and a significant drop in breathing. In our study, fatigue actions were collected first, and then the different micro-Doppler characteristics produced by human activity were used to classify and recognize the fatigue action using the fine-tuning convolution neural network (FT-CNN) model. The collected signals in the breathing mode were preprocessed to judge whether the person was fatigued according to the estimated value of the respiratory rate. Data in different environments were collected to verify the proposed method. Our results showed that the accuracy of fatigue detection can reach 91.8% in the laboratory environment and 87.3% in realistic scenarios.