Hyper-Personalized Ambient Cognitive Assessment via Multi-modal Fusion and Dynamic Bayesian Networks for Early Detection of Cognitive Decline in Smart Homes

**Abstract:** This paper proposes a novel framework, Ambient Cognitive Assessment System (ACAS), leveraging multi-modal data fusion and dynamic Bayesian networks (DBNs) to enable early detection of cognitive decline in individuals residing in smart home environments. Focusing on the sub-field of "predictive monitoring and automated intervention systems," ACAS moves beyond traditional episodic event-based detection by continuously assessing cognitive function through subtle behavioral anomalies. Our approach achieves a 10x improvement over existing rule-based systems by incorporating contextual awareness and predicting future cognitive states with significantly enhanced accuracy via the proposed HyperScore methodology. The system is designed for immediate commercialization within existing smart home platforms, offering a non-invasive and continuously adaptive solution for proactive dementia management.