Published In
IEEE Sensors Journal
Document Type
Post-Print
Publication Date
5-2023
Subjects
Hospitals -- Monitoring -- Sensors, Cleaning -- Guidelines, Microcontrollers -- Tracking
Abstract
Hospital-acquired infections are a major cause of death worldwide, and poor hand hygiene compliance is a primary reason for their spread. This paper proposes an artificial intelligence, microcontroller, and sensor-based system that monitors and improves staff hand hygiene compliance at various critical points in a hospital. The system uses a Convolutional Neural Network (CNN) to detect and track if staff have followed the WHO hand rub/hand wash guidelines at alcohol dispensers, hospital sinks, and patient beds. The system also uses RFID tags, vibration motors, LEDs, and a central server to identify staff, alert them of their cleaning requirements, monitor their cleaning activity, and report compliance data. We obtain an accuracy of 90.6% in classifying all steps of the WHO-stipulated hand wash/hand rub guidelines and a testing accuracy of 89.8% on Ivanovs et al.’s dataset. The system ensures that hospital staff stay compliant to all WHO hand hygiene guidelines, saving countless lives.
Rights
© Copyright the author(s)
Locate the Document
DOI
10.1109/JSEN.2023.3271297
Persistent Identifier
https://archives.pdx.edu/ds/psu/40088
Citation Details
Published as: Shrimali, S., & Teuscher, C. (2023). A Novel Deep Learning, Camera, and Sensor-based System for Enforcing Hand Hygiene Compliance in Healthcare Facilities. IEEE Sensors Journal.
Description
This is the author’s version of a work that was accepted for publication in IEEE Sensors Journal.Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in IEEE Sensors Journal.