Remote Drowsiness Detection Based on the mmWave FMCW Radar

Drowsiness can lead to inefficiency and major disasters; thus it is important to address it in both academia and industry. Despite multiple types of research in this field, a nonintrusive classifier system for detecting drowsiness in real-time under a natural environment without specific stimulation is lacking. This study develops a real-time drowsiness detection system using a 77 GHz millimeter-wave (mmWave) frequency-modulated continuous wave radar. Specifically, firstly, a real-time mmWave processing module is proposed, which can adaptively suppress both stationary and non-stationary clutters. Secondly, a feature extraction module based on a hybrid of handcrafted and machine learning (ML) features is propsoed to obtain a holistic view of mmWave-based vital signals, in which ML features represent linear and temporal changes. Thirdly, a drowsiness classification model is proposed based on feature fusion and extreme gradient boosting algorithms, thus classifying a user's state into two categories: non-drowsy and drowsy. To confirm the proposed system's performance, it is validated on a self-collected dataset (n = 28). The experimental results show the following (i) the accuracy of heart rate evaluation is 96.4%, and (ii) based on 10-fold cross-validation, the proposed system gains a detection accuracy of 82.9% and outperforms the state-of-the-art approaches.