Estimation of Clinical Workload and Patient Activity Using Deep Learning and Optical Flow
| Autor: | Thanh Nguyen-Duc, Andrew Tay, David Chen, John Tan Nguyen, Jessica Lyall, Maria De Freitas, Peter Y. Chan |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction Electrical and Electronic Engineering Instrumentation Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) |
| Zdroj: | IEEE Sensors Letters. 6:1-4 |
| ISSN: | 2475-1472 |
| DOI: | 10.1109/lsens.2022.3181600 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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