Drowsiness detection system using deep learning based data fusion approach

Drowsy driving is a major cause of road accidents worldwide, and detecting drowsiness in drivers is critical to improving road safety. In this work, we developed a system for detecting drowsiness using Electroencephalogram and Electrocardiogram signals and a combination of 2D convolutional neural networks and a fuzzy neural network. The Electroencephalogram and Electrocardiogram signals were processed using convolutional neural networks to extract features, and a fuzzy neural network was used to classify the features and detect drowsiness. Our results show that our system can detect drowsiness with a high degree of accuracy, making it a reliable and efficient tool for improving road safety. The proposed system helps to reduce the number of accidents caused by drowsy driving and improve road safety for all drivers.