论文标题
使用深度学习和光流估算临床工作量和患者活动
Estimation of Clinical Workload and Patient Activity using Deep Learning and Optical Flow
论文作者
论文摘要
使用热成像的非接触式监测已越来越提出，以监测医院的患者恶化，最近以检测19次大流行期间的发烧和感染。在这封信中，我们提出了一种新的方法来估计患者运动并使用类似的技术设置观察临床工作量，但与开源对象检测算法（Yolov4）和光流相结合。患者运动估计用于近似患者的搅动和镇静，而工人运动则用作护理人员工作量的替代物。通过比较从重症监护病房记录的患者视频与临床工人记录的临床煽动分数的32000多个帧相比，可以说明了性能。
Contactless monitoring using thermal imaging has become increasingly proposed to monitor patient deterioration in hospital, most recently to detect fevers and infections during the COVID-19 pandemic. In this letter, we propose a novel method to estimate patient motion and observe clinical workload using a similar technical setup but combined with open source object detection algorithms (YOLOv4) and optical flow. Patient motion estimation was used to approximate patient agitation and sedation, while worker motion was used as a surrogate for caregiver workload. Performance was illustrated by comparing over 32000 frames from videos of patients recorded in an Intensive Care Unit, to clinical agitation scores recorded by clinical workers.
