M Thirunavukkarasan, Krishna Priya Remamany, M P Vaishnnave, S A Sahaaya Arul Mary, Ganesh Gopal Devarajan, Rajendra Prasad Mahapatra
To address the growing need for intelligent retrieval and personalized content generation in consumer healthcare electronic devices, this work proposes a secure, scalable, and AI-enhanced framework integrating wearable IoMT devices with edge–fog–cloud infrastructures. The system leverages quantum blockchain with Quantum Key Distribution (QKD) for tamper-proof storage of sensor data and applies a hybrid Practical Byzantine Fault Tolerance (pBFT) and Proof of Work (PoW) consensus for low-latency validation. At the edge layer, consumer medical devices, such as smart watches, smart patches, and mobile health assistants perform preliminary anomaly detection using lightweight BiLSTM-CNN models integrated with Quantum Neural Networks (QNN). When emergencies or anomalies are detected, the fog layer handles intelligent data retrieval and prioritization based on task urgency, network quality, and energy constraints. The cloud layer supports long-term storage and AI-driven content generation, such as personalized health summaries, alerts, and predictive reports. The architecture enables fast retrieval of user-specific biomedical data across consumer platforms and generates real-time decision support notifications through smartphones, wearables, and connected home healthcare centers. The simulation results demonstrate improved responsiveness, security, and retrieval efficiency compared to traditional IoMT architectures. This framework positions consumer healthcare electronic devices as intelligent, context-aware, and secure systems capable of real-time predictive assistance, data retrieval, and adaptive content generation for smart living environments.