  Agitation is a symptom that communicates distress in people living with
dementia (PwD), and that can place them and others at risk. In a long term care
(LTC) environment, care staff track and document these symptoms as a way to
detect when there has been a change in resident status to assess risk, and to
monitor for response to interventions. However, this documentation can be
time-consuming, and due to staffing constraints, episodes of agitation may go
unobserved. This brings into question the reliability of these assessments, and
presents an opportunity for technology to help track and monitor behavioural
symptoms in dementia. In this paper, we present the outcomes of a 2 year
real-world study performed in a dementia unit, where a multi-modal wearable
device was worn by $20$ PwD. In line with a commonly used clinical
documentation tool, this large multi-modal time-series data was analyzed to
track the presence of episodes of agitation in 8-hour nursing shifts. The
development of a baseline classification model (AUC=0.717) on this dataset and
subsequent improvement (AUC= 0.779) lays the groundwork for automating the
process of annotating agitation events in nursing charts.
