Automated Depth Video Monitoring For Fall Reduction : A Case Study
Josh Brown Kramer, Lucas Sabalka, Benjamin Rush, Katherine J. Jones, Tegan Nolte · 2020
Patient falls are a common, costly, and serious safety problem in hospitals and healthcare facilities. We have created a system that reduces falls by using computer vision to monitor fall risk patients and alert staff of unsafe behavior before a fall happens. This paper is a companion and followup to "Modeling bed exit likelihood in a camera-based automated video monitoring application," in which we describe the Ocuvera system. [1] Here additional details are provided on that system and its processes. We report clinical results, detail practices used to iterate rapidly and effectively on a massive video database, discuss details of our people tracking algorithms, and discuss the engineering effort required to support the new Azure Kinect depth camera.