AI-powered Alzheimer’s diagnosis: Integrating cognitive monitoring, IoT, and secure edge computing

Abstract

This study proposes a privacy-preserving, multi-modal AI framework for the early detection of Alzheimer’s disease (AD), addressing the limitations of conventional single-modal diagnostic systems. The model fuses heterogeneous data sources, including physiological signals from wearable IoT devices, neuroimaging biomarkers extracted from T1-weighted MRI scans, and environmental context derived from smart home sensors. A hybrid architecture incorporating temporal CNN-LSTM networks, 3D ResNet models, attention layers, and graph neural networks is employed to extract and integrate cross-modal features. Federated learning with differential privacy (ε = 1.0) enables secure and decentralized training across distributed healthcare nodes, ensuring compliance with HIPAA and GDPR. Experimental validation on real-world datasets such as ADNI-4 and IoT-HOME shows a diagnostic accuracy of 97.3%, with a 12% improvement in recall over single-modality baselines. The system achieves sub-150 millisecond inference latency on resource-constrained edge devices through quantization and kernel pruning. Results demonstrate robust convergence, high interpretability via SHAP explanations, and scalability in heterogeneous clinical environments. The framework offers a technically robust, ethically aligned, and practically deployable solution for real-time, edge-enabled Alzheimer’s monitoring in both institutional and home-care settings.