Drowsiness Detection

Drowsiness detection research aims to develop accurate and real-time systems for identifying driver or worker fatigue to improve safety and productivity across various sectors. Current research focuses on leveraging computer vision techniques (e.g., facial landmark detection, vision transformers, convolutional neural networks) and electroencephalography (EEG) signals, often employing machine learning models like graph convolutional networks, LSTM autoencoders, and ensemble methods to analyze these data streams. These advancements hold significant potential for reducing accidents caused by drowsiness, enhancing workplace safety, and improving overall human performance in demanding tasks.