  Interpreting the environmental, behavioural and psychological data from
in-home sensory observations and measurements can provide valuable insights
into the health and well-being of individuals. Presents of neuropsychiatric and
psychological symptoms in people with dementia have a significant impact on
their well-being and disease prognosis. Agitation in people with dementia can
be due to many reasons such as pain or discomfort, medical reasons such as side
effects of a medicine, communication problems and environment. This paper
discusses a model for analysing the risk of agitation in people with dementia
and how in-home monitoring data can support them. We proposed a semi-supervised
model which combines a self-supervised learning model and a Bayesian ensemble
classification. We train and test the proposed model on a dataset from a
clinical study. The dataset was collected from sensors deployed in 96 homes of
patients with dementia. The proposed model outperforms the state-of-the-art
models in recall and f1-score values by 20%. The model also indicates better
generalisability compared to the baseline models.
