Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
This work addresses the need for automated monitoring of chronic neurological diseases like dementia, though it appears incremental with preliminary findings.
The paper tackled the problem of detecting early-stage dementia by analyzing behavioral signatures from sensor data, finding subtle differences in sleep quality and wandering patterns between patients and healthy controls.
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.