  Agitation is one of the neuropsychiatric symptoms with high prevalence in
dementia which can negatively impact the Activities of Daily Living (ADL) and
the independence of individuals. Detecting agitation episodes can assist in
providing People Living with Dementia (PLWD) with early and timely
interventions. Analysing agitation episodes will also help identify modifiable
factors such as ambient temperature and sleep as possible components causing
agitation in an individual. This preliminary study presents a supervised
learning model to analyse the risk of agitation in PLWD using in-home
monitoring data. The in-home monitoring data includes motion sensors,
physiological measurements, and the use of kitchen appliances from 46 homes of
PLWD between April 2019-June 2021. We apply a recurrent deep learning model to
identify agitation episodes validated and recorded by a clinical monitoring
team. We present the experiments to assess the efficacy of the proposed model.
The proposed model achieves an average of 79.78% recall, 27.66% precision and
37.64% F1 scores when employing the optimal parameters, suggesting a good
ability to recognise agitation events. We also discuss using machine learning
models for analysing the behavioural patterns using continuous monitoring data
and explore clinical applicability and the choices between sensitivity and
specificity in-home monitoring applications.
