# Video-Based Inpatient Fall Risk Assessment: A Case Study

arXiv: https://arxiv.org/abs/2106.07565 · HTML: https://mlsanity.com/paper/2106.07565/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences](https://mlsanity.com/paper/1810.07538/) (2018)
- [Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation](https://mlsanity.com/paper/1907.02161/) (2019)
- [In-Bed Pose Estimation: Deep Learning with Shallow Dataset](https://mlsanity.com/paper/1711.01005/) (2017)
- [Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM](https://mlsanity.com/paper/2007.07773/) (2020)
- [SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience](https://mlsanity.com/paper/2004.00797/) (2020)
- [Falls Prediction Based on Body Keypoints and Seq2Seq Architecture](https://mlsanity.com/paper/1908.00275/) (2019)

## Built on by (most cited first, 2 of 2)

- [In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains](https://mlsanity.com/paper/2111.15124/) (2021)
- [Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study](https://mlsanity.com/paper/2202.10704/) (2022)

## Abstract (arXiv)

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.