Silva G.D.TSineth H.M.GConstantine S.A.D.N.C.HDissanayake M.EPandithage, DLokuliyana, S2026-10-032025-12-17G. D. T. Silva, H. M. G. Sineth, S. A. D. N. C. H. Constantine, M. E. Dissanayake, D. Pandithage and S. Lokuliyana, "Federated Edge Intelligence for Adaptive Health Prediction in Elderly Care," 2025 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2025, pp. 903-908, doi: 10.1109/ICAAIC64647.2025.11330950.979-833156587-9https://rda.sliit.lk/handle/123456789/5311This research presents the development and evaluation of an IoT-based nursing home monitoring system designed to enhance elderly healthcare through real-time detection, predictive alerts, and caregiver support. The system integrates multimodal sensing with edge and server-level machine learning to monitor three critical areas: sleep quality, fall detection, and emotion recognition in dementia patients. Hardware components, including a Raspberry Pi and non-intrusive sensors, were combined with lightweight neural networks to ensure low-latency processing. Detailed model optimizations, including 8-bit quantized TensorFlow Lite MobileNetV2 and CNN-LSTM pipelines, enabled on-device inference within 80-120 ms. A caregiver dashboard and mobile application provided intuitive visualization of real-time alerts and long-term health reports. Benchmarking against threshold-based and classical ML baselines demonstrated 9-18% performance improvements and 56% lower alert latency. Experimental evaluation demonstrated promising outcomes, with fall detection achieving 95% accuracy, emotion detection 91%, and sleep monitoring 90% reliability. System uptime reached 98%, and alerts were delivered within two seconds in 96% of cases. These results indicate that the proposed solution can effectively reduce caregiver burden, improve resident safety, and establish a scalable framework for intelligent elderly healthcare monitoring.enedge computingelderly monitoringemotion recognitionfall detectionIoT-based healthcarenursing home systemsleep quality analysisConference Paperdoi: 10.1109/ICAAIC64647.2025.11330950