Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments

Hao Ren; Guanwen Lin; Wanmin Lian; Fengshi Jing; Ya Zou; Yunting Liu; Qingpeng Zhang; Kaishun Wu; Weibin Cheng

doi:10.1109/JIOT.2025.3569223

Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments

Hao Ren (Co-first Author)
, Guanwen Lin (Co-first Author)
, Wanmin Lian (Co-first Author)
, Fengshi Jing
, Ya Zou
, Yunting Liu
, Qingpeng Zhang
, Kaishun Wu^*
, Weibin Cheng^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

6 Citations (Scopus)

Abstract

This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections. © 2014 IEEE.

Original language	English
Pages (from-to)	33056-33066
Journal	IEEE Internet of Things Journal
Volume	12
Issue number	16
Online published	12 May 2025
DOIs	https://doi.org/10.1109/JIOT.2025.3569223
Publication status	Published - 15 Aug 2025
Externally published	Yes

Funding

The authors acknowledge the support of the Department of Healthcare-associated Infection Management, Guangdong Second Provincial General Hospital. We would also like to thank all the students who participated in this study. This work was funded by the Guangzhou Municipal Science and Technology Project (Grant Nos. 2024A03J1074, 2023A03J0286, 2024A03J0927, and 2024A03J1067), the Science and Technology Development Fund (FDCT) of the Macao Special Administrative Region (Grant No. 0064/2024/RIB2), the Guangdong Provincial Key Laboratory of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things (Grant No. 2023B1212010007), and the Guangdong Provincial Medical Science Research Fund (Grant No. B2024030).

Research Keywords

Computer Vision
Hand Hygiene Compliance
Healthcare Settings
Machine Learning
Multi-Camera Systems

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10.1109/JIOT.2025.3569223

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title = "Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments",

abstract = "This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections. {\textcopyright} 2014 IEEE.",

keywords = "Computer Vision, Hand Hygiene Compliance, Healthcare Settings, Machine Learning, Multi-Camera Systems",

author = "Hao Ren and Guanwen Lin and Wanmin Lian and Fengshi Jing and Ya Zou and Yunting Liu and Qingpeng Zhang and Kaishun Wu and Weibin Cheng",

year = "2025",

month = aug,

day = "15",

doi = "10.1109/JIOT.2025.3569223",

language = "English",

volume = "12",

pages = "33056--33066",

journal = "IEEE Internet of Things Journal",

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}

Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments. / Ren, Hao (Co-first Author); Lin, Guanwen (Co-first Author); Lian, Wanmin (Co-first Author) et al.
In: IEEE Internet of Things Journal, Vol. 12, No. 16, 15.08.2025, p. 33056-33066.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments

AU - Ren, Hao

AU - Lin, Guanwen

AU - Lian, Wanmin

AU - Jing, Fengshi

AU - Zou, Ya

AU - Liu, Yunting

AU - Zhang, Qingpeng

AU - Wu, Kaishun

AU - Cheng, Weibin

PY - 2025/8/15

Y1 - 2025/8/15

N2 - This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections. © 2014 IEEE.

AB - This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections. © 2014 IEEE.

KW - Computer Vision

KW - Hand Hygiene Compliance

KW - Healthcare Settings

KW - Machine Learning

KW - Multi-Camera Systems

UR - https://www.scopus.com/pages/publications/105005171999

UR - https://www.scopus.com/pages/publications/105005171999#tab=impact

U2 - 10.1109/JIOT.2025.3569223

DO - 10.1109/JIOT.2025.3569223

M3 - RGC 21 - Publication in refereed journal

SN - 2327-4662

VL - 12

SP - 33056

EP - 33066

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

IS - 16

ER -

Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments

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