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There is a pressing need to automatically understand the state andprogression of chronic neurological diseases such as dementia. The emergence ofstate-of-the-art sensing platforms offers unprecedented opportunities forindirect and automatic evaluation of disease state through the lens ofbehavioural monitoring. This paper specifically seeks to characterisebehavioural signatures of mild cognitive impairment (MCI) and Alzheimer'sdisease (AD) in the \textit{early} stages of the disease. We introduce bespokebehavioural models and analyses of key symptoms and deploy these on a noveldataset of longitudinal sensor data from persons with MCI and AD. We presentpreliminary findings that show the relationship between levels of sleep qualityand wandering can be subtly different between patients in the early stages ofdementia and healthy cohabiting controls.

Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data