用可解释AI分析传感器数据，早发现认知衰退迹象
The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline
- 通过智能家庭传感器收集患者日常行为数据
- 基于一年30名患者的实测数据，实现行为变化检测
- 模型结果可解释，帮助医生信任并辅助诊断
轻度认知障碍（MCI）影响60岁以上人群的12-18%。MCI患者虽有认知功能障碍，但日常生活能力尚可维持。尽管部分患者可能发展为痴呆，但预测进展仍面临挑战，因缺乏可靠指标。日常活动（ADLs）执行中的行为变化可提示病情进展。传感器化智能家居与可穿戴设备为非侵入式、连续监测提供了新方案。然而，现有机器学习模型透明度不足，难以获得临床信任。本文介绍欧盟资助的SERENADE项目，旨在利用可解释AI方法，检测并解释与认知衰退相关的行为变化。项目计划收集30名独居MCI患者为期一年的数据，以支持临床决策，探索早期痴呆检测的新路径。
原文摘要 · Abstract (English)
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.
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