Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2601-2604. doi: 10.1109/EMBC46164.2021.9630857.
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.
住院患者跌倒在医院和医疗保健设施中是一个严重的安全问题。最近在视频分析用于患者监测方面的进展为通过持续的活动监测来降低这种风险提供了一种非侵入性的途径。然而，卧床跌倒风险评估系统在文献中受到的关注较少。之前的大多数研究都集中在跌倒事件检测上，而没有考虑可能表明即将发生住院患者跌倒的情况。在这里，我们提出了一种基于视频的系统，可以监测患者跌倒的风险，并提醒工作人员注意不安全行为，以帮助防止跌倒发生。我们提出了一种方法，利用最近在人体定位和骨骼姿势估计方面的进展，从模拟环境中记录的视频帧中提取空间特征。我们证明，身体姿势可以被有效地识别，并为跌倒风险评估提供有用的证据。这项工作强调了基于视频的模型在分析感兴趣的行为方面的优势，并展示了这样的系统如何为医疗保健专业人员提供足够的响应时间来满足患者的需求，这对于跌倒干预计划的发展是必要的。
