Automatic falls detection in hospital-room context

Abstract

This paper presents a framework for the monitoring of hospitalized people, including fall detection capabilities, using an environmentally mounted depth imaging sensor. The purpose is to characterize the fall event, depending on the location of the person when the fall event happens. In particular, we distinguish two basic starting point conditions: fall from standing position (e.g. due to blood pressure failure) and fall out of bed (e.g. due to agitation). To achieve this goal, we exploit the context information to adaptively extract the person's silhouette and then reliably tracking the trajectory. If a fall occurs, the system is capable of recognize this event on the basis of the inferred starting condition. The current implementation has been tested on available online datasets and on a self-made dedicated dataset. In this latter dataset, we have included falls from standing position and falls out of bed, even in presence of occlusions.