A real-time drivers' status monitoring scheme with safety analysis

Smart transportation and smart healthcare are considered as essential Smart City applications. The emerging light-weight sensors facilitate real-time monitoring drivers' status in various applications especially safety and healthcare. As such, the statistics reveals that >60% of adult drivers felt sleepy while driving, and drunk drivers are found in >40% of traffic accidents. In this paper, an electrocardiogram (ECG) based Drivers' Status Monitoring (ECG-DSM) system is developed to detect drowsy and drunk driving. The proposed ECG-DSM extracted similarities of ECG signals under normal, drowsy and drunk conditions, and the corresponding feature vector was built. The classifier is expected to alert drivers accurately and timely to prevent traffic accidents. Hence, the classifier's trade-off between accuracy and detection time was analysed by adjusting the dimensionality of feature vector. Safety analysis using Monte Carlo simulation was carried out to determine the best classifier under practical working environment. The results demonstrated that the best classifier for ECG-DSM achieves 9 1 % of average accuracy and 4.2s of detection time, and it can prevent >92% of vehicle collisions due to drowsy and drunk driving. The proposed work will contribute to road traffic safety and save $50 billion US dollars on the cost of traffic injuries.