论文标题
通过传感器数据得出的行为模型来检测早期痴呆的特征
Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
论文作者
论文摘要
迫切需要自动了解慢性神经系统疾病（如痴呆症）的状态和进展。最先进的传感平台的出现为通过行为监测的视角提供了空前的间接和自动评估疾病状态评估的机会。本文特别旨在表征该病的\ textit {早期}阶段中轻度认知障碍（MCI）和阿尔茨海默氏病（AD）的行为特征。我们介绍了定制的行为模型和关键症状的分析，并将其部署在具有MCI和AD的人的纵向传感器数据的新型数据集中。我们提出的初步发现表明，在痴呆症早期和健康同居对照的早期，患者之间的睡眠质量和流浪水平之间的关系可能会微妙。
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the \textit{early} stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset of longitudinal sensor data from persons with MCI and AD. We present preliminary findings that show the relationship between levels of sleep quality and wandering can be subtly different between patients in the early stages of dementia and healthy cohabiting controls.
