A Quantum Machine Learning framework for Driver Drowsiness Detection using Biopotential Signals and Head Movement Analysis
Published in IEEE ICWITE 2024, 2024
Road accidents claim numerous lives annually, with drowsiness identified as a primary catalyst for a substantial portion of these incidents. This study addresses this critical issue by introducing an innovative approach to gauge human drowsiness levels during driving. The primary objective is to introduce a novel deep-learning technique capable of detecting various alertness levels — awake, drowsy, and very sleepy — while driving.
A hybrid model is proposed, leveraging Convolutional Neural Networks (CNN) in conjunction with an Attention-based Quantum Long Short-Term Memory (QLSTM) network. The designed model employs different biopotential signals, including electroencephalogram (EEG), facial electromyography (EMG), pulse rate, and head movement, to discern a person’s alertness level. The proposed model achieves detection rates of 99%, 98.5%, and 99% for awake, drowsy, and very sleepy states respectively, offering a promising solution to mitigate the impact of drowsiness-related accidents.
Recommended citation: De, S., & Gupta, A. K. (2024). "A Quantum Machine Learning framework for Driver Drowsiness Detection using Biopotential Signals and Head Movement Analysis." IEEE ICWITE.
Download Paper