Differential Self-Attention in 1-D CNNs for Driver Inattention Detection Using Multimodal Biosignals

[Graphical abstarct sensors] Driver inattention detection is crucial for road safety, as stress can impair cognitive functions and increase accident risk. Recent advances in wearable technology have led to an increase in the use of multimodal physiological signals for driver inattention detection. Integrating attention mechanisms into these systems has shown promise in enhancing inattention detection. However, attention features can be affected by noise in the data, presenting a significant challenge. To address this, we propose a multimodal Differential Self-Attention based 1D Convolutional Neural Network (MDSA-1DCNN) to reduce noise in attention features. In this study, we evaluate the effectiveness of MDSA-1DCNN on multimodal 1D biosignals obtained from textile electrodes, collecting single-lead Electrocardiogram (ECG) (256 Hz) and Respiration (RSP) (128 Hz) data from 15 healthy participants in two driving states: Normal and inattention. The raw data were divided into non-overlapping segments of 10, 15, and 20 seconds and preprocessed using the Neurokit Toolbox. These processed segments from each modality were then passed through convolutional blocks to extract temporal features. Self-attention was applied to these features, followed by a differential attention layer to reduce noise. The resulting features were fed into dense layers to classify driver inattention state. The proposed MDSA-1DCNN approach achieved a weighted F-Score of 73.23% and an average accuracy of 78.51% on the validation set using Leave-One-Subject-Out Cross-Validation. Future work will explore the utilization of data from multiple sensors and investigate sensor fusion techniques.