This article presents a scalable IoT-based architecture for continuous and passive monitoring of human behavior in home environments, designed as a technical foundation for future dementia risk assessment systems. The architecture addresses three fundamental challenges: achieving room-level spatial localization without privacy-invasive methods, balancing temporal resolution with bandwidth efficiency in continuous data streams, and enabling multi-institutional model development under GDPR constraints. The system integrates (1) wearable BLE sensors with infrared room-level localization; (2) edge computing gateways with local preprocessing and machine learning; (3) a three-channel data architecture that simultaneously achieves full 1 s temporal resolution for machine learning training, low-latency real-time visualization, and 41.2% network bandwidth reduction; and (4) a federated learning framework enabling collaborative model development without data sharing between institutions. Technical validation in two apartments (three participants, 7 days) demonstrated: 97.6% room-level localization accuracy using infrared beacons; less than 7 s end-to-end latency for 99.5% of critical events; and 98.5% deduplication accuracy in multi-gateway configurations. Federated learning simulation demonstrates algorithmic convergence (84.3% IID, 79.8% non-IID) and workflow feasibility, establishing a foundation for future production deployment. Cost analysis shows approximately €490 for initial implementation and approximately €55 monthly operation, representing substantially lower costs than existing research systems. The work establishes architectural and technical feasibility, as well as system-level economic viability, of continuous home monitoring for behavioral analysis within the evaluated residential scenarios. Clinical validation of diagnostic capabilities through longitudinal studies with validated cognitive assessments and patients with mild cognitive impairment remains to be studied in future work.
Rosen Ivanov (Sun,) studied this question.