Emotion-Augmented Neuro-Symbolic and Quantum-Inspired Adaptive Pain Therapy Using Federated Biofeedback AR/VR Systems

Assessment of physiological distress is required for effective pain management, but so also is the interpretation of emotional state. In this paper, we provide a novel framework named Emotion-Augmented Neuro-Symbolic and Quantum Inspired Reinforcement Learning (EA-NSAI-QRL) that targets providing personalized and real time pain therapy in biofeedback driven AR/VR environment. An attention weighted system fusion of physiological signals (HRV, EEG, GSR) to emotional cue (facial expression, voice, posture) is presented. With rule-based reasoning, transparent reasoning, and accelerated therapy optimization, the Neuro Symbolic AI and Quantum Inspired Reinforcement Learning respectively. The model updates there are guaranteed to be privacy preserved in Federated Learning across the decentral nodes. An experimental result on a synthetic multimodal dataset shows that proposed model can improve the average pain reduction by 2.14 points, and emotion score by 0.15, as well as can converge 30% faster in choosing therapy policy compared to classical RL. In addition, over 95% of the decision consistency with expert defined symbolic rules was maintained with the system outperforming baseline models including CNNRNN and federated random forest on effectiveness of therapeutic efficacy by up to 28%. These results substantiated that the system can deliver safe, intelligible and secure digital therapeutics with a scalable path for deployment into postoperative pain management in real world.