Multimodal Wearable-Based Automated Driver Inattention State Assessment Using Multidevices and Novel Cross-Modal Attention Framework

Driver inattention detection remains a critical challenge in driver's well-being, requiring robust systems that can distinguish stress-induced mental load during naturalistic driving. Current approaches face limitations in multiple wearable-based data fusion and real-time biosignals assessment. This letter proposes a novel cross-squeeze-and-excitation convolution neural network (CNN) framework to process simultaneously acquired multiple wearable from 15 participants in controlled driving scenarios. The multimodal signals are applied to multistage attention mechanisms [electrocardiogram (ECG)$\leftrightarrow$electrodermal activity (EDA), ECG $\rightarrow$ EDA, EDA $\rightarrow$ ECG] with 1D-CNN blocks, optimized for 10-s signal segments. The proposed approach is able to classify drive inattention state. It is observed that ECG $\rightarrow$ EDA attention achieves 76.54% average accuracy using leave-one-subject-out cross validation, outperforming unimodal approaches by 12.4% and bidirectional attention by 4.8%. Feature visualizations confirm enhanced pattern discrimination in inattention conditions. This letter advances driver health monitoring systems through effective wearable integration and adaptive feature weighting, with potential for edge deployment and clinical stress assessment applications