Multimodal Drowsiness Detection Methods using Machine Learning Algorithms

Drowsiness is a main threat to drivers, and induces inefficiency in various fields, such as industry and education. In this paper, monitoring drowsiness is investigated in which five healthy volunteers participated in an experiment to elicit drowsiness. The subjects were asked to limit their sleep duration to only two to three hours and restrict caffeine intake during the 24 hours prior to the experiment. In the experiment, a one-channel electrocardiogram (ECG) and a single-channel electroencephalogram (EEG) were simultaneously recorded. The ECG and EEG features were extracted and fed into machine learning, random forest, multilayer perceptron, and support vector machine algorithms. Various feature combinations were utilized to train the algorithms, and random forest yielded the best performance at about 90% accuracy, precision, and recall, with 10-second epochs in the ECG and EEG.