The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline
Gabriele Civitarese, Michele Fiori, Andrea Arighi, Daniela Galimberti, Graziana Florio, Claudio Bettini
TL;DR
SERENADE addresses the need for early detection of cognitive decline by continuously monitoring ADL-related behaviors in real homes via sensor networks. It uses unsupervised, self-supervised learning and anomaly detection together with explainable AI to derive digital biomarkers from unlabeled data, avoiding reliance on ground-truth annotations. The framework combines sensing, data governance, and a clinician-facing telemedicine dashboard to deliver interpretable insights and support diagnoses. Initial deployment in 18 homes with plans to scale to larger cohorts ($30$ subjects over $1$ year) demonstrates feasibility and informs future improvements in privacy-preserving remote monitoring for MCI.
Abstract
Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functional loss. While MCI may progress to dementia, predicting this transition remains a clinical challenge due to limited and unreliable indicators. Behavioral changes, like in the execution of Activities of Daily Living (ADLs), can signal such progression. Sensorized smart homes and wearable devices offer an innovative solution for continuous, non-intrusive monitoring ADLs for MCI patients. However, current machine learning models for detecting behavioral changes lack transparency, hindering clinicians' trust. This paper introduces the SERENADE project, a European Union-funded initiative that aims to detect and explain behavioral changes associated with cognitive decline using explainable AI methods. SERENADE aims at collecting one year of data from 30 MCI patients living alone, leveraging AI to support clinical decision-making and offering a new approach to early dementia detection.