AI-Driven multimodal sensing and secure data frameworks for next-generation biomedical wearable health monitoring systems

The convergence of artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and wireless body area networks (WBANs) has introduced promising opportunities for continuous biomedical health monitoring. However, translating these technologies into robust wearable platforms requires overcoming challenges across intelligent multimodal diagnosis, edge-efficient processing, and end-to-end privacy preservation. This paper presents the AI-Driven Multimodal Secure Health Monitoring Architecture (AIMSHA), a four-tier framework structured across body sensor, edge gateway, fog aggregation, and secure cloud layers. AIMSHA couples multi-biosignal interpretation (EEG, EMG, ECG, and photoplethysmography) with explainable AI (XAI) transparency dashboards and lightweight hierarchical federated learning (FL). To protect distributed model updates against inference vulnerabilities, differential privacy noise perturbation is paired with attribute-based encryption (ABE) access controls and secure aggregation. Furthermore, a lightweight edge intrusion detection system mitigates malicious packet injection, while a permissioned blockchain ledger provides tamper-resistant provenance tracking for global model checkpoints and consent authorisations. Extensive evaluations across six task-aligned physiological benchmark cohorts demonstrate that the underlying MediFuse-DL backbone achieves a mean diagnostic Area Under the ROC Curve (AUC) of 0.974. AIMSHA achieves a 41.3% reduction in communication overhead compared to centralized transmission baselines, maintains continuous sensor-tier monitoring autonomy exceeding 72 hours, and satisfies formal differential privacy guarantees ( ε = 1.97 , δ = 10⁻⁵ ) over 100 federated communication rounds. Finally, we analyze software-as-a-medical-device regulatory considerations and clinical translation pathways, positioning AIMSHA as a viable preclinical baseline for secure, privacy-preserving remote patient monitoring. Clinical trial number Not applicable.