Stanford University

Intelligent Hand Hygiene

Overview

We are designing an intelligent computer vision system for remote monitoring, assessment, and support of hand hygiene in hospital environments. By improving hand-wishing quality and compliance, we hope to reduce the rate of hospital-acquired infections due to contaminated equipment, bed linens, or improper patient handling.

We are investigating the use of multiple sensors for the detection, measurement, and evaluation of hand hygiene in controlled laboratory environments, hospital corridors, and patient bedroom units. Our goal is to automatically detect missed hand hygiene events and intervene in real-time to prevent potentially contaminating events. This can include physical contact with patients, handling of biologically hazardous materials, or insufficient hand washing quality.

Our sensors are deployed at two major healthcare partners: Intermountain Healthcare and Lucile Packard Children's Hospital (LPCH) at Stanford. There is continuous active research exploring computer vision technologies and clinical outcome improvement. Our findings have been published in both medical and machine learning venues.

If your hospital organization would like to join this groundbreaking collaboration, we welcome any questions and are happy to facilate discussion. Contact information is below.


We have partnered with Intermountain's Healthcare Transformation Lab where we have deployed 3D depth sensors in several patient rooms. With the help of Intermountain, we are using live data streams to teach our computer vision algorithms to discern events of clinical relevance such as hand hygiene events and patient interaction.


In collaboration with Lucile Packard Children's Hospital, we have installed state-of-the-art sensors in over 10 patient rooms and multiple corridors for hand hygiene activities. Our machine learning algorithms learn routine movement patterns by staff and individual hand hygiene behaviors by guests.

Dispenser Usage Detection

With the help of artificial neural networks, our method uses deep learning to automatically detect usage of an alcohol-based sanitizer dispenser from challenging ceiling-mounted top views.

Physical Space Analytics

Intuitive, qualitative results analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. Red regions denote high traffic areas while blue denotes low traffic regions.

Privacy Safe Assessment

To comply with privacy regulations, we use de-identified depth images instead of color photos to track and analyze hand hygiene compliance. Our method can track multiple clinicians throughout a hospital ward.

People


Terrry Platchek
Lucile Packard

Amit Singh
Lucile Packard

William Beninati
Intermountain

Julia Lee
CERC

Alexandre Alahi
Stanford AI Lab

Serena Yeung
Stanford AI Lab

Albert Haque
Stanford AI Lab

Michelle Guo
Stanford AI Lab

Josh Beal
Stanford AI Lab

Publications

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, Amit Singh, Jeffrey Jopling,
Lance Downing, William Beninati, Terry Platchek, Arnold Milstein, Li Fei-Fei

Machine Learning in Healthcare Conference (MLHC), Proceedings of Machine Learning Research
August 2017

PDF arXiv Conference


Vision-Based Hand Hygiene Monitoring in Hospitals

Serena Yeung, Alexandre Alahi, Zelun Luo, Boya Peng, Albert Haque, Amit Singh, Terry Platchek,
Arnold Milstein, Li Fei-Fei

American Medical Informatics Association (AMIA) Annual Symposium
November 2016

Conference


Towards Viewpoint Invariant 3D Human Pose Estimation

Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, Li Fei-Fei

European Conference on Computer Vision (ECCV)
October 2016

PDF Website arXiv Conference


Recurrent Attention Models for Person Identification

Albert Haque, Alexandre Alahi, Li Fei-Fei

Conference on Computer Vision and Pattern Recognition (CVPR)
June 2016

PDF Website Conference


Vision-Based Hand Hygiene Monitoring in Hospitals

Serena Yeung, Alexandre Alahi, Zelun Luo, Boya Peng, Albert Haque, Amit Singh, Terry Platchek,
Arnold Milstein, Li Fei-Fei

Workshop on Machine Learning in Healthcare, Neural Information Processing Systems (NIPS)
December 2015

PDF Conference

Contact

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