Multimodal Driver Drowsiness Detection Using Facial Expressions and Ear-EEGs With a Lightweight Auto-Denoising Network

Integrating computer vision and physiological analysis in driver drowsiness detection (DDD) is a promising technology for accurately identifying drowsy states while driving, thereby preventing potentially dangerous accidents. This study proposes a multimodal DDD system with a deep neural network that combines computer vision-based face expression analysis and electroencephalogram (EEG) data analysis. Key contributions include: 1) providing a comprehensive hardware, firmware, and software design for the DDD system to acquire behind-the-ear (BTE) EEG signals, rather than conventional scalp EEGs, due to their convenience and practicality; 2) proposing a powerful and lightweight GAN-based auto-denoising method to eliminate artifacts from EEG signals during signal acquisition, significantly influencing the quality of the obtained result; 3) developing a multimodal DDD network by combining EEG analysis and computer vision-based face expression identification to improve performance in monitoring and early detection of the driver’s drowsiness while engaging in traffic. The study employs the relative root mean squared error (RRMSE) in both temporal and spectral domains to quantitatively assess the performance of the proposed approaches in artifact removal. The proposed GAN-based auto-denoising network outperforms other comparable approaches, with an RRMSE (temporal) of 0.210 and RRMSE (spectral) of 0.161. The proposed trained multimodal model with GAN-based auto-denoising is superior to other models with different denoising approaches in driver drowsiness detection across all five-evaluation metrics, with an accuracy of 95.33%, specificity of 95.48%, sensitivity of 95.17%, precision of 95.47%, and an F1-score of 95.32%. The experimental results demonstrate the practicality and feasibility of our proposed DDD system.