Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction

Understanding and predicting human emotional and physiological states using wearable sensors has critical applications in stress monitoring, mental health assessment, and affective computing. In this study, we present a novel Multi - Task Physics - Informed Neural Network (PINN) that simultaneously performs Electrodermal Activity (EDA) prediction and emotion classification using the publicly available WESAD dataset. Our model integrates psychological self - reports (PANAS and SAM) with a physics - inspired differential formulation of EDA dynamics, enforcing biophysically grounded constraints through a custom loss that balances data - driven learning and physiological interpretability. The architecture supports dual outputs -- regression for EDA and classification for emotional states -- trained under a unified multi - task framework. Evaluated via 5 - fold cross - validation, the proposed method achieves an average EDA RMSE of 0.0362, Pearson correlation (r) of 0.9919, and F1 - score of 94.08%, outperforming both classical baselines (e.g., SVR, XGBoost) and ablated variants such as emotion - only and EDA - only models. Comparative ablation and multi - task experiments show that including both physics constraints and emotion prediction enhances generalization, reduces overfitting, and leads to physiologically consistent outputs. Moreover, the learned physical parameters -- decay rate (α), emotion influence weights (β), and temporal scaling (γ) -- remain interpretable and stable across folds, confirming the alignment between the model's latent representation and known stress - response theory. This is the first work to introduce a multi - task PINN architecture for wearable affective computing, bridging black - box deep learning and domain knowledge. Our framework lays the groundwork for interpretable, multimodal, and deployable systems in healthcare and human - computer interaction.