基于视频的住院患者跌倒风险评估：一项案例研究
Video-Based Inpatient Fall Risk Assessment: A Case Study
AI总结：
针对现有研究多聚焦跌倒事件检测、缺乏跌倒前风险评估的问题，本文提出基于视频的住院患者跌倒风险评估系统，结合人体定位与骨架姿态估计提取特征，可识别不安全行为并提前预警，为跌倒干预提供支持。
摘要：
住院患者跌倒是医院及医疗机构中的严重安全问题。用于患者监测的视频分析技术的最新进展，提供了一种非侵入性途径，可通过持续活动监测降低跌倒风险。然而，床旁跌倒风险评估系统在现有文献中受到的关注较少。既往大多数研究聚焦于跌倒事件检测，未考虑可能预示住院患者即将发生跌倒的各类情况。本文提出了一套基于视频的系统，能够监测患者的跌倒风险，并就不安全行为向医护人员发出警报，从而在跌倒发生前实现预防。我们提出的方法利用人体定位与骨架姿态估计领域的最新进展，从模拟环境中录制的视频帧中提取空间特征。实验证明，人体姿态能够被有效识别，且可为跌倒风险评估提供有效依据。本研究凸显了基于视频的模型在分析目标行为方面的优势，并展示了此类系统如何为医护人员提供充足的响应时间以满足患者需求，这对于跌倒干预项目的开发至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
