Drowsiness and Lethargy Detection Using Machine Learning Techniques

Abstract

Drowsiness has severe effects on the safety of human life. The worldwide death rate due to drowsy driving is quite alarming. As the implementation of artificial intelligence (AI) is growing faster, this paper describes an attempt to implement machine learning (ML) to detect drowsiness. 120 videos of 60 participants are collected from the Real-Life Drowsiness Video Dataset made by a research team of the Vision-Learning-Mining Lab from the University of Texas at Arlington. Then Eye Aspect Ratio, Mouth Aspect Ratio, Pupil Circularity, and Mouth Aspect Ratio Over Eye Aspect Ratio, Nose Length, Chin Length, Nose Length Over Chin Length Ratio are extracted as features of each participant using the 3D Face-Mesh 468 facial landmarks system from those videos. After that, each feature is normalized by its mean and standard deviation. Then the CSV dataset is generated using seven initial and seven normalized features. A total of 30000 instances are there in the dataset. A total of eight classification algorithms are implemented to build the model. The dataset is split such that the individual in the train set will not be in the test set to test the proposed model's ability to predict drowsiness for new faces. 5-fold cross-validation is implemented to measure performance for each algorithm. Convolutional Neural Network (CNN) yields maximum accuracy (91.63%). The state of any individual's eye closing, rapid eye blinking, yawning, putting a hand on the mouth during yawning, and head posing too much up or down can be detected as drowsiness by the proposed model.