  Inpatient falls are a serious safety issue in hospitals and healthcare
facilities. Recent advances in video analytics for patient monitoring provide a
non-intrusive avenue to reduce this risk through continuous activity
monitoring. However, in-bed fall risk assessment systems have received less
attention in the literature. The majority of prior studies have focused on fall
event detection, and do not consider the circumstances that may indicate an
imminent inpatient fall. Here, we propose a video-based system that can monitor
the risk of a patient falling, and alert staff of unsafe behaviour to help
prevent falls before they occur. We propose an approach that leverages recent
advances in human localisation and skeleton pose estimation to extract spatial
features from video frames recorded in a simulated environment. We demonstrate
that body positions can be effectively recognised and provide useful evidence
for fall risk assessment. This work highlights the benefits of video-based
models for analysing behaviours of interest, and demonstrates how such a system
could enable sufficient lead time for healthcare professionals to respond and
address patient needs, which is necessary for the development of fall
intervention programs.
