Smart Aging Anomaly Detection (SAAD) ( MSCA H2020)

A prestigious Marie Curie Individual Fellowship dedicated to pioneering Physical AI and Multimodal Data Fusion methodologies within large-scale Ambient Assisted Living (AAL) systems. The framework systematically integrates ambient smart-home sensors to continuously evaluate and process human kinetic and behavioral actions.

Successfully developed predictive behavioral modeling and anomaly identification algorithms capable of capturing subtle shifts in activities of daily living (ADLs). The resulting framework enables the early categorization and clinical tracking of cognitive impairment and dementia, significantly delaying the need for traditional care models.

This European project uses smart home sensors and artificial intelligence to help elderly individuals live safely and independently in their own homes. By quietly monitoring everyday habits, the system learns regular behavioral patterns and sounds an alarm if it detects unusual anomalies, allowing for the early detection of cognitive decline or memory conditions like dementia.