重症监护病房中的过程监测：使用非侵入式移动传感器通过活动分析评估患者的活动能力。
Process Monitoring in the Intensive Care Unit: Assessing Patient Mobility Through Activity Analysis with a Non-Invasive Mobility Sensor.
作者信息
Reiter Austin, Ma Andy, Rawat Nishi, Shrock Christine, Saria Suchi
机构信息
The Johns Hopkins University, Baltimore, MD, USA.
Johns Hopkins Medical Institutions, Baltimore, MD, USA.
出版信息
Med Image Comput Comput Assist Interv. 2016 Oct;9900:482-490. doi: 10.1007/978-3-319-46720-7_56. Epub 2016 Oct 2.
Throughout a patient's stay in the Intensive Care Unit (ICU), accurate measurement of patient mobility, as part of routine care, is helpful in understanding the harmful effects of bedrest [1]. However, mobility is typically measured through observation by a trained and dedicated observer, which is extremely limiting. In this work, we present a video-based automated mobility measurement system called . Our main contributions are: (1) a novel multi-person tracking methodology designed for complex environments with occlusion and pose variations, and (2) an application of human-activity attributes in a clinical setting. We demonstrate NIMS on data collected from an active patient room in an adult ICU and show a high inter-rater reliability using a weighted Kappa statistic of 0.86 for automatic prediction of the highest level of patient mobility as compared to clinical experts.
在患者入住重症监护病房（ICU）的整个过程中，作为常规护理的一部分，准确测量患者的活动能力有助于了解卧床休息的有害影响[1]。然而，活动能力通常是由训练有素且专注的观察者通过观察来测量的，这极具局限性。在这项工作中，我们展示了一种名为 的基于视频的自动活动能力测量系统。我们的主要贡献包括：（1）一种专为存在遮挡和姿势变化的复杂环境设计的新型多人跟踪方法，以及（2）人类活动属性在临床环境中的应用。我们在从成人ICU的一个活跃病房收集的数据上展示了NIMS，并使用加权Kappa统计量0.86显示了与临床专家相比，自动预测患者最高活动能力水平时具有较高的评分者间可靠性。
Zotero 插件