AI-driven wearable health devices with health-aware control and secure Prognostics: Experimental and Simulation-Based Validation

The integration of health-aware control (HAC) and prognostics within wearable health devices (WHDs) presents a transformative opportunity for personalized healthcare. This study introduces an AI-driven, four-tier WHD framework for real-time physiological monitoring, predictive analytics, and adaptive system control. The architecture integrates biosensors, deep learning models (CNN, LSTM, Transformer), reinforcement learning–based HAC, federated learning for decentralized intelligence, and blockchain-enhanced secure communication. Experimental validation was conducted using benchmark biosignal datasets (PhysioNet, MIMIC-III) and embedded WHD prototypes, while OMNeT++ simulations evaluated large-scale deployments. Transformer models achieved 96.1% classification accuracy with 30 ms latency; RL-based adaptive sampling reduced power consumption by 50%; and the federated security framework reached 98.9% tampering detection with 90% privacy risk reduction. Wi-Fi consistently outperformed BLE in latency and scalability. These results confirm the feasibility of integrating HAC and prognostics in WHDs, enabling proactive monitoring, energy-efficient operation, and privacy-preserving personalized healthcare.