Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction
This work addresses the need for interpretable emotion recognition systems in healthcare and human-computer interaction applications, though it represents an incremental advancement by applying existing PINN concepts to a new multi-task problem in this domain.
This study tackled the problem of predicting human emotional and physiological states from wearable sensor data by developing a Multi-Task Physics-Informed Neural Network (PINN) that simultaneously performs Electrodermal Activity (EDA) prediction and emotion classification. The model achieved an average EDA RMSE of 0.0362, Pearson correlation of 0.9919, and F1-score of 94.08%, outperforming classical models and ablated variants.
Understanding and predicting human emotional and physiological states using wearable sensors has important applications in stress monitoring, mental health assessment, and affective computing. This study presents a novel Multi-Task Physics-Informed Neural Network (PINN) that performs Electrodermal Activity (EDA) prediction and emotion classification simultaneously, using the publicly available WESAD dataset. The model integrates psychological self-report features (PANAS and SAM) with a physics-inspired differential equation representing EDA dynamics, enforcing biophysically grounded constraints through a custom loss function. This loss combines EDA regression, emotion classification, and a physics residual term for improved interpretability. The architecture supports dual outputs for both tasks and is trained under a unified multi-task framework. Evaluated using 5-fold cross-validation, the model achieves an average EDA RMSE of 0.0362, Pearson correlation of 0.9919, and F1-score of 94.08 percent. These results outperform classical models such as SVR and XGBoost, as well as ablated variants like emotion-only and EDA-only models. In addition, the learned physical parameters including decay rate (alpha_0), emotional sensitivity (beta), and time scaling (gamma) are interpretable and stable across folds, aligning with known principles of human physiology. This work is the first to introduce a multi-task PINN framework for wearable emotion recognition, offering improved performance, generalizability, and model transparency. The proposed system provides a foundation for future interpretable and multimodal applications in healthcare and human-computer interaction.