Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene ComplianceReport as inadecuate




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Platchek, Terry; Milstein, Arnold; Fei-Fei, Li Presented at: Machine Learning for Healthcare (MLHC), Northeastern University, Massachusetts, August 18- 19, 2017 Publication date: 2017

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a first step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.

Reference EPFL-CONF-230221





Author: Haque, Albert; Guo, Michelle; Alahi, Alexandre; Yeung, Serena; Luo, Zelun; Rege, Alisha; Jopling, Jeffrey; Downing, Lance; Beninat

Source: https://infoscience.epfl.ch/record/230221?ln=en







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