The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline

2025-04-11

AI Summary

This study addresses the dual challenges of early dementia progression detection and limited clinical interpretability in mild cognitive impairment (MCI). We propose the first explainable AI (XAI) behavioral monitoring framework specifically designed for dynamic MCI evolution. Leveraging unobtrusive, longitudinal data from smart home sensors and wearables, our approach fuses heterogeneous multimodal sensor streams and introduces an attention-based XAI model integrated with counterfactual reasoning to enable interpretable activities of daily living (ADL) anomaly detection and cognitively grounded attribution visualization. We conducted a 12-month real-world study with 30 community-dwelling MCI patients, yielding the first publicly available, fully annotated behavioral benchmark dataset with clinically interpretable labels for cognitive decline. Clinical validation demonstrates 86.7% accuracy in progression prediction and a 92% increase in clinicians’ trust in model-generated decision support.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Detect cognitive decline using sensor data
• Improve transparency in MCI behavioral monitoring
• Enable early dementia detection via explainable AI

Innovation

• Sensor-based smart homes for MCI monitoring
• Explainable AI for behavioral change detection
• Long-term data collection to predict dementia