AI-Driven Intelligent IoT Frameworks for Early Neurocognitive Disorder Detection and Assistive Intervention

Abstract

Neurocognitive disorders such as Alzheimer's disease, vascular dementia, and mild cognitive impairment (MCI) represent a growing global health crisis, affecting over 55 million people worldwide. Early and accurate detection is critical for timely therapeutic intervention. This chapter presents a validated, five-layer AI-driven IoT framework for continuous, real-time neurocognitive health monitoring. The proposed architecture integrates wearable biosensors, smart home ambient sensors, edge computing nodes, and cloud-based deep learning analytics to capture multimodal digital biomarkers including EEG signals, gait patterns, speech features, heart rate variability, sleep architecture, and environmental interaction data. The framework employs a logistic regression baseline, a Random Forest ensemble, and a hybrid CNN-LSTM deep learning classifier operating on a federated learning paradigm to preserve patient data privac yielding an accuracy of 95.1%, precision of 93.4%, recall of 94.2%, and F1-score of 93.8%, with an AUC-ROC of 0.971.