A Dual-Branch Convolutional Neural Network with Gated Recurrent Units Network for Enhanced Multimodal Stress Monitoring from Wearable Physiological Signals

Chronic mental stress poses severe threats to both physical and psychological well being, highlighting the importance of continuous monitoring through wearable technologies. Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) signals provide reliable, noninvasive, and cost-effective means for stress assessment. In this work, we present a lightweight deep learning framework that integrates dual-branch convolutional neural networks (CNN) with gated recurrent units (GRU) for real-time stress detection from multimodal BVP and EDA signals. The model is evaluated on the publicly available WESAD dataset using subject-independent leave-one-subject-out validation and achieves state of the art performance: 99.27\% accuracy, 99.97\% F1-score, 99.68\% AUC, and 98.40\% Cohen’s $\kappa$. To address class imbalance, a sliding window augmentation strategy is employed, significantly boosting the minority class performance. With only 0.43M parameters and minimal computational cost, the proposed architecture is optimized for deployment on resource-constrained wearable devices, offering a robust solution for real-world stress monitoring.