Automated Depth Video Monitoring for Fall Reduction : A Case Study
Abstract
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.
Cite
Text
Kramer et al. "Automated Depth Video Monitoring for Fall Reduction : A Case Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00155Markdown
[Kramer et al. "Automated Depth Video Monitoring for Fall Reduction : A Case Study." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/kramer2020cvprw-automated/) doi:10.1109/CVPRW50498.2020.00155BibTeX
@inproceedings{kramer2020cvprw-automated,
title = {{Automated Depth Video Monitoring for Fall Reduction : A Case Study}},
author = {Kramer, Joshua Brown and Sabalka, Lucas and Rush, Benjamin and Jones, Katherine and Nolte, Tegan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {1188-1196},
doi = {10.1109/CVPRW50498.2020.00155},
url = {https://mlanthology.org/cvprw/2020/kramer2020cvprw-automated/}
}