This study proposes a harmonized architecture that integrates Large Language Models (LLMs), wearable Internet of Medical Things (IoMT) devices, and edge-based AI for real-time, personalized mental wellness monitoring. We introduce a novel 10-step harmonization framework that systematically aligns multimodal sensing (e.g., electro-dermal activity, heart rate variability), privacy-preserving data pipelines, LLM-enabled contextual reasoning, and adaptive feedback delivery. The framework supports scalable use cases including stress detection, sleep optimization, mood forecasting, and cognitive behavioral therapy (CBT) personalization. By embedding federated learning, differential privacy, and Explainability tools (e.g., SHAP, LIME), our system ensures fairness, transparency, and on-device adaptability. The study validates this approach using high-fidelity datasets (e.g., WESAD, DANLIR) and provides a rigorous evaluation protocol addressing performance, latency, fairness, and interpretability. This work contributes a blueprint for ethical, intelligent, and scalable digital health systems that can deliver inclusive and context-aware mental wellness interventions across diverse deployment settings.
Bukhari et al. (Mon,) studied this question.