Video-Based Inpatient Fall Risk Assessment: A Case Study

TL;DR

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 is proposed and it is demonstrated how such a system could enable sufficient lead time for healthcare professionals to respond and address patient needs.

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.

Figures (6)

• Figure 1: Overview of the proposed fall risk monitoring system. Given a single RGB image collected with a custom camera system placed in the ceiling, the system generates 2D human pose predictions. Next, the relative position of the human and the bed is computed to predict the risk of falling.
• Figure 2: Human and bed localisation. A dynamic and fast instance segmentation approach is used to localise the region of interest.
• Figure 3: Selected samples of human actions collected in the simulated dataset. Top: Not at risk of falling. Bottom: At risk of falling.
• Figure 4: Representation of the human key points detected in a selected image.
• Figure 5: Representation of the distance feature estimation. Head and knees detected by the pose estimation algorithm are marked as green dots, and all lines represent the contour of the detected bed.