A Novel Hybrid Explainable AI Framework for Multimodal Pain-Aware Human Robot Interaction

The explainable human-robot system for real-time pain assessment presented in this study combines autonomous care-robot behavior with multimodal affective sensing (RGB facial expressions, temperature data, and physiological signals). The approach uses the BioVid dataset to train and evaluate Tri-Modal Explanation Fusion (TMEF), which combines SHAP, LIME, and dual-stream Grad-CAM to produce consistent and understandable explanations for pain predictions. By identifying cross-modal inconsistencies prior to sending out alerts, a Signal-Attention Alignment (SAA) technique further reduces false alarms. By providing human-readable explanations for high-pain conditions, the robot enhances interaction safety and clinical transparency. The current gap where current pain-detection algorithms remain highly predictive but insufficiently explainable is filled by preliminary results that show sustained multimodal fusion, dependable interpretability, and higher user confidence. This work advances healthcare-oriented HRI by unifying explainability, continuous pain monitoring, and ethical robotic decision-making.