Estimation of Clinical Workload and Patient Activity Using Deep Learning and Optical Flow

Abstract

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 32 000 frames from the videos of patients recorded in an intensive care unit to clinical agitation scores recorded by clinical workers.