Video-Based Inpatient Fall Risk Assessment: A Case Study

TL;DR

This paper presents a novel video-based system using human pose estimation to assess inpatient fall risk proactively, enabling timely intervention and potentially reducing hospital falls.

Contribution

It introduces a new approach leveraging skeleton pose estimation for fall risk assessment, focusing on pre-fall behavior detection in inpatient settings.

Findings

Body positions effectively indicate fall risk.

Video analysis can provide early alerts for fall prevention.

System demonstrates potential for real-time monitoring.

Abstract

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.