  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.
