Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

Albert Haque, Michelle Guo, Alexandre Alahi, Serena Yeung, Zelun Luo, Alisha Rege, Jeffrey Jopling, Lance Downing, William Beninati, Amit Singh, Terry Platchek, Arnold Milstein, Li Fei-Fei
; Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:75-87, 2017.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v68-haque17a, title = {Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance}, author = {Albert Haque and Michelle Guo and Alexandre Alahi and Serena Yeung and Zelun Luo and Alisha Rege and Jeffrey Jopling and Lance Downing and William Beninati and Amit Singh and Terry Platchek and Arnold Milstein and Li Fei-Fei}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {75--87}, year = {2017}, editor = {Finale Doshi-Velez and Jim Fackler and David Kale and Rajesh Ranganath and Byron Wallace and Jenna Wiens}, volume = {68}, series = {Proceedings of Machine Learning Research}, address = {Boston, Massachusetts}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/haque17a/haque17a.pdf}, url = {http://proceedings.mlr.press/v68/haque17a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance %A Albert Haque %A Michelle Guo %A Alexandre Alahi %A Serena Yeung %A Zelun Luo %A Alisha Rege %A Jeffrey Jopling %A Lance Downing %A William Beninati %A Amit Singh %A Terry Platchek %A Arnold Milstein %A Li Fei-Fei %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-haque17a %I PMLR %J Proceedings of Machine Learning Research %P 75--87 %U http://proceedings.mlr.press %V 68 %W PMLR %X 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.
APA
Haque, A., Guo, M., Alahi, A., Yeung, S., Luo, Z., Rege, A., Jopling, J., Downing, L., Beninati, W., Singh, A., Platchek, T., Milstein, A. & Fei-Fei, L.. (2017). Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance. Proceedings of the 2nd Machine Learning for Healthcare Conference, in PMLR 68:75-87

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