[Picture 1] Textile sensors enable non-invasive health monitoring, crucial for ensuring road safety by conducting mental well-being checks for drivers. Assessing driver stress with multimodal data from textile electrodes requires effectively integrating and interpreting diverse physiological signals and kinematic data. In this study, we evaluated textile electrodes and cross-attention mechanisms for assessing driver stress using multimodal data. Electrocardiography and Respiration data were collected from 15 healthy volunteers wearing smart shirts in two driving scenarios. Signals were sampled at 256Hz and 128Hz, respectively, with vehicle data also recorded. Segmented Physiological & Vehicle data enter separate networks, 1D Convolutional layers for signals and fully connected layers for vehicle data. Cross-attention fuses Physiological data, these features are combined with vehicle data for stress classification using sigmoid. The proposed approach is able to classify driver stress states using multimodal data, achieving an average accuracy of 79% and an average F-score of 75%. The integration of a cross-attention mechanism facilitates the capture of inter-modality information.

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

Driver distraction has proven to be among the top contributory factors in road crashes worldwide. The involvement of drivers in secondary activities, such as phone usage, conversation with co-passengers, reaching for objects, eating, and so forth, have been shown to be significant causes of in-vehicle driver distraction. Text messaging using mobile phones while driving can be detrimental to the primary driving task as it invokes manual, visual, and cognitive distractions. Numerous studies in the past have analyzed the effect of texting on driver distraction using driving simulators. This study followed a field operation test–based approach to simulate real road conditions and evaluate the impact of texting on driving behavior. The study was conducted at the connected autonomous vehicle testbed at the Indian Institute of Technology Hyderabad, using an instrumented test vehicle. Data on gaze parameters and vehicle kinematics were recorded using eye-tracker glasses and a high-end global positioning system (GPS) data logger. Thirty-one participants drove on the pre-defined path at the testbed under baseline and texting conditions. The findings demonstrated that the texting task significantly affected fixation count, mean speed, and lateral acceleration. The texting task led to a reduction in mean fixation counts, mean speed, and mean lateral acceleration by 44.37%, 34%, and 46.72%, respectively. The age and driving experience of the participants had a significant effect on their behavior. The results indicate the critical potential of texting-based driver distraction and suggest the enforcement of stringent rules for texting while driving to create a safer road environment.