Detecting and Monitoring Behavioural Patterns in Individuals with Cognitive Disorders in the Home Environment with Partial Annotations

Weisong Yang, Rafael Poyiadzi, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raúl Santos-Rodríguez, James Selwood, Niall Twomey

Abstract: The need to automatically monitor the state and progression of chronic neurological diseases such as dementia, together with the emergence of state-of-the-art sensing platforms for the home environment offer unprecedented opportunities for automatic behavioural monitoring as a proxy of disease state. However, when these platforms have been deployed, data challenges, including the lack of reliable annotations, limit the applicability of standard machine learning techniques. This chapter specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. We introduce bespoke machine learning techniques accounting for partial annotations to produce behavioural metrics of key symptoms and use these on a novel dataset of longitudinal sensor data from persons with MCI and AD.