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

Lakshmikanthaiah Sm (), Mamatha G () and Ajith Padyana ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 3, 1518-1524

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.

Keywords: Alzheimer’s disease; Cognitive monitoring; Edge AI; Federated learning. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijirss.com/index.php/ijirss/article/view/6830/1356 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:3:p:1518-1524:id:6830

Access Statistics for this article

International Journal of Innovative Research and Scientific Studies is currently edited by Natalie Jean

More articles in International Journal of Innovative Research and Scientific Studies from Innovative Research Publishing
Bibliographic data for series maintained by Natalie Jean ().