research-article

RFWash: a weakly supervised tracking of hand hygiene technique

Authors:

Abdelwahed Khamis,

Branislav Kusy,

Chun Tung Chou,

Mary-Louise McLaws,

Wen HuAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 572 - 584

https://doi.org/10.1145/3384419.3430733

Published: 16 November 2020 Publication History

Abstract

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAIs). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this paper, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of < 8% when trained on 10-second segments, which reduces manual labelling overhead by ≈ 67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.

References

[1]

2009. WHO Guidelines on Hand Hygiene in Health Care. Published by World Health Organisation. Retrieved from: whqlibdoc.who.int/publications/009.pdf. (2009).

[2]

2016. Healthcare-associated infections: data and statistics. Published by Center for Disease Control and Prevention. (2016).

[3]

2020 (accessed July 1, 2020). Ti IWR1443BOOST. "https://www.ti.com/tool/IWR1443BOOST". (2020 (accessed July 1, 2020)).

[4]

Heba Abdelnasser, Moustafa Youssef, and Khaled A Harras. 2015. Wigest: A ubiquitous wifi-based gesture recognition system. In 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 1472--1480.

[5]

Sari Awwad, Sanjay Tarvade, Massimo Piccardi, and David J Gattas. 2019. The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene. International Journal for Quality in Health Care 31, 1 (2019), 36--42.

[6]

John M Boyce. 2011. Measuring healthcare worker hand hygiene activity: current practices and emerging technologies. Infection control and hospital epidemiology 32, 10 (2011), 1016--1028.

[7]

Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein, and Li Fei-Fei. 2018. Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images. Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 (2018).

[8]

I. R. Daniels and B. I. Rees. 1999. Handwashing: simple, but effective. Annals of the Royal College of Surgeons of England 81, 2 (Mar 1999), 117--118. https://www.ncbi.nlm.nih.gov/ /10364970 10364970[pmid].

[9]

Lijie Fan, Tianhong Li, Yuan Yuan, and Dina Katabi. 2020. In-Home Daily-Life Captioning Using Radio Signals. In European Conference on Computer Vision (ECCV) 2020.

[10]

Piyali Goswami, Sandeep Rao, Sachin Bharadwaj, and Amanda Nguyen. 2019. Real-time multi-gesture recognition using 77 GHz FMCW MIMO single chip radar. In 2019 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 1--4.

[11]

Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. 2006. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on Machine learning. ACM, 369--376.

Digital Library

[12]

Albert Haque, Michelle Guo, Alexandre Alahi, Serena Yeung, Zelun Luo, Alisha Rege, Jeffrey Jopling, Lance Downing, William Beninati, Amit Singh, et al. 2017. Towards vision-based smart hospitals: A system for tracking and monitoring hand hygiene compliance. Proceedings of Machine Learning Research (18--19 Aug 2017).

[13]

Chengkun Jiang, Junchen Guo, Yuan He, Meng Jin, Shuai Li, and Yunhao Liu. 2020. mmVib: micrometer-level vibration measurement with mmwave radar. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1--13.

Digital Library

[14]

Yen Lee Angela Kwok, Michelle Callard, and Mary-Louise McLaws. 2015. An automated hand hygiene training system improves hand hygiene technique but not compliance. American journal of infection control 43, 8 (2015), 821--825.

[15]

Yen Lee Angela Kwok, Craig P Juergens, and Mary-Louise McLaws. 2016. Automated hand hygiene auditing with and without an intervention. American journal of infection control 44, 12 (2016), 1475--1480.

[16]

Hong Li, Shishir Chawla, Richard Li, Sumeet Jain, Gregory D Abowd, Thad Starner, Cheng Zhang, and Thomas Plotz. 2018. Wristwash: towards automatic handwashing assessment using a wrist-worn device. In Proceedings of the 2018 ACM International Symposium on Wearable Computers. 132--139.

Digital Library

[17]

Yunan Li, Qiguang Miao, Kuan Tian, Yingying Fan, Xin Xu, Rui Li, and Jianfeng Song. 2016. Large-scale gesture recognition with a fusion of rgb-d data based on the c3d model. In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 25--30.

[18]

Bingbin Liu, Michelle Guo, Edward Chou, Rishab Mehra, Serena Yeung, N Lance Downing, Francesca Salipur, Jeffrey Jopling, Brandi Campbell, Kayla Deru, et al. 2018. 3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities. In Machine Learning for Healthcare Conference. 17--29.

[19]

Hu Liu, Sheng Jin, and Changshui Zhang. 2018. Connectionist temporal classification with maximum entropy regularization. In Advances in Neural Information Processing Systems. 831--841.

[20]

David Fernández Llorca, Ignacio Parra, Miguel Ángel Sotelo, and Gerard Lacey. 2011. A vision-based system for automatic hand washing quality assessment. Machine Vision and Applications 22, 2 (2011), 219--234.

Digital Library

[21]

Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A. Stankovic, Niki Trigoni, and Andrew Markham. 2020. See through Smoke: Robust Indoor Mapping with Low-Cost MmWave Radar. In Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services (MobiSys '20). 14--27.

Digital Library

[22]

Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign language recognition using WiFi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 23.

Digital Library

[23]

AR Marra and MB Edmond. 2014. New technologies to monitor healthcare worker hand hygiene. Clinical Microbiology and Infection 20, 1 (2014), 29--33.

[24]

Maryanne McGuckin and John Govednik. 2015. A review of electronic hand hygiene monitoring: considerations for hospital management in data collection, healthcare worker supervision, and patient perception. Journal of Healthcare Management 60, 5 (2015), 348--361.

[25]

Pedro Melgarejo, Xinyu Zhang, Parameswaran Ramanathan, and David Chu. 2014. Leveraging directional antenna capabilities for fine-grained gesture recognition. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 541--551.

Digital Library

[26]

Marco Mercuri, Ilde Rosa Lorato, Yao-Hong Liu, Fokko Wieringa, Chris Van Hoof, and Tom Torfs. 2019. Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor. Nature Electronics 2, 6 (2019), 252--262.

[27]

Pavlo Molchanov, Xiaodong Yang, Shalini Gupta, Kihwan Kim, Stephen Tyree, and Jan Kautz. 2016. Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4207--4215.

[28]

Avishek Patra, Philipp Geuer, Andrea Munari, and Petri Mähönen. 2018. mm-Wave Radar Based Gesture Recognition: Development and Evaluation of a Low-Power, Low-Complexity System. In Proceedings of the 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems. 51--56.

Digital Library

[29]

Lisa L Pineles, Daniel J Morgan, Heather M Limper, Stephen G Weber, Kerri A Thom, Eli N Perencevich, Anthony D Harris, and Emily Landon. 2014. Accuracy of a radiofrequency identification (RFID) badge system to monitor hand hygiene behavior during routine clinical activities. American journal of infection control 42, 2 (2014), 144--147.

[30]

Xingshuai Qiao, Tao Shan, Ran Tao, Xia Bai, and Juan Zhao. 2019. Separation of human micro-Doppler signals based on short-time fractional Fourier transform. IEEE Sensors Journal 19, 24 (2019), 12205--12216.

[31]

Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing 45, 11 (1997), 2673--2681.

Digital Library

[32]

Muhammad Shahzad and Shaohu Zhang. 2018. Augmenting User Identification with WiFi Based Gesture Recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 134.

Digital Library

[33]

Karly A Smith, Clément Csech, David Murdoch, and George Shaker. 2018. Gesture recognition using mm-wave sensor for human-car interface. IEEE Sensors Letters 2, 2 (2018), 1--4.

[34]

Jocelyn A Srigley, Colin D Furness, G Ross Baker, and Michael Gardam. 2014. Quantification of the Hawthorne effect in hand hygiene compliance monitoring using an electronic monitoring system: a retrospective cohort study. BMJ Qual Saf 23, 12 (2014), 974--980.

[35]

Yonglong Tian, Guang-He Lee, Hao He, Chen-Yu Hsu, and Dina Katabi. 2018. RF-based fall monitoring using convolutional neural networks. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1--24.

Digital Library

[36]

Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. 2015. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision. 4489--4497.

Digital Library

[37]

Raghav H Venkatnarayan, Griffin Page, and Muhammad Shahzad. 2018. Multiuser gesture recognition using WiFi. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 401--413.

Digital Library

[38]

Aditya Virmani and Muhammad Shahzad. 2017. Position and orientation agnostic gesture recognition using wifi. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 252--264.

Digital Library

[39]

Saiwen Wang, Jie Song, Jaime Lien, Ivan Poupyrev, and Otmar Hilliges. 2016. Interacting with soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology. ACM, 851--860.

Digital Library

[40]

Xin Yang, Jian Liu, Yingying Chen, Xiaonan Guo, and Yucheng Xie. 2020. MU-ID: Multi-user Identification Through Gaits Using Millimeter Wave Radios. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 2589--2598.

[41]

Yinggang Yu, Dong Wang, Run Zhao, and Qian Zhang. 2019. RFID based real-time recognition of ongoing gesture with adversarial learning. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems. 298--310.

Digital Library

[42]

Shigeng Zhang, Chengwei Yang, Xiaoyan Kui, Jianxin Wang, Xuan Liu, and Song Guo. 2019. ReActor: Real-time and Accurate Contactless Gesture Recognition with RFID. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 1--9.

[43]

Zhenyuan Zhang, Zengshan Tian, and Mu Zhou. 2018. Latern: Dynamic continuous hand gesture recognition using FMCW radar sensor. IEEE Sensors Journal 18, 8 (2018), 3278--3289.

[44]

Henry Zhong, Salil S Kanhere, and Chun Tung Chou. 2016. WashInDepth: Lightweight Hand Wash Monitor Using Depth Sensor. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ACM, 28--37.

Digital Library

[45]

Y. Zou, J. Xiao, J. Han, K. Wu, Y. Li, and L. M. Ni. 2017. GRfid: A Device-Free RFID-Based Gesture Recognition System. IEEE Transactions on Mobile Computing 16, 2 (Feb 2017), 381--393.

Digital Library

Cited By

Liu YZhang JChen YWang WYang SNa XSun YHe Y(2025)Real-Time Continuous Activity Recognition With a Commercial mmWave RadarIEEE Transactions on Mobile Computing10.1109/TMC.2024.348381324:3(1684-1698)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3483813
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Wang SMei LLiu RJiang WYin ZDeng XHe T(2025)Multi-Modal Fusion Sensing: A Comprehensive Review of Millimeter-Wave Radar and Its Integration With Other ModalitiesIEEE Communications Surveys & Tutorials10.1109/COMST.2024.339800427:1(322-352)Online publication date: Feb-2025
https://doi.org/10.1109/COMST.2024.3398004
Show More Cited By

Index Terms

RFWash: a weakly supervised tracking of hand hygiene technique
1. Human-centered computing
  1. Ubiquitous and mobile computing

Recommendations

Harmony: A Hand Wash Monitoring and Reminder System using Smart Watches
MOBIQUITOUS'15: proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services on 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Hand hygiene compliance is extremely important in hospitals, clinics and food businesses. Caregivers' compliance with hand hygiene is one of the most effective tools in preventing healthcare associated infections (HAIs) in hospitals and clinics. In food ...
Real-time and location-based hand hygiene monitoring and notification: proof-of-concept system and experimentation

Rising infection rates in health care cause complications for the patient, extended hospital stay, financial difficulties and even death. One of the crucial factors that reduce those infections is better hand hygiene. Due to the lack of automated ...
Hand Hygiene Quality Assessment Using Image-to-Image Translation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Abstract
Hand hygiene can reduce the transmission of pathogens and prevent healthcare-associated infections. Ultraviolet (UV) test is an effective tool for evaluating and visualizing hand hygiene quality during medical training. However, due to various ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

852 pages

ISBN:9781450375900

DOI:10.1145/3384419

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SenSys '20

Sponsor:

SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems

November 16 - 19, 2020

Virtual Event, Japan

Acceptance Rates

Overall Acceptance Rate 198 of 990 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
803
Total Downloads

Downloads (Last 12 months)94
Downloads (Last 6 weeks)9

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu YZhang JChen YWang WYang SNa XSun YHe Y(2025)Real-Time Continuous Activity Recognition With a Commercial mmWave RadarIEEE Transactions on Mobile Computing10.1109/TMC.2024.348381324:3(1684-1698)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3483813
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Wang SMei LLiu RJiang WYin ZDeng XHe T(2025)Multi-Modal Fusion Sensing: A Comprehensive Review of Millimeter-Wave Radar and Its Integration With Other ModalitiesIEEE Communications Surveys & Tutorials10.1109/COMST.2024.339800427:1(322-352)Online publication date: Feb-2025
https://doi.org/10.1109/COMST.2024.3398004
Cui KYang QShen LZheng YXiao FHan J(2024)Towards ISAC-Empowered mmWave Radars by Capturing Modulated VibrationsIEEE Transactions on Mobile Computing10.1109/TMC.2024.344340423:12(13787-13803)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3443404
Xu WYang HChen JLuo CZhang JZhao YLi W(2024)WashRing: An Energy-Efficient and Highly Accurate Handwashing Monitoring System via Smart RingIEEE Transactions on Mobile Computing10.1109/TMC.2022.322729923:1(971-984)Online publication date: Jan-2024
https://doi.org/10.1109/TMC.2022.3227299
Stadelmayer THassab YServadei LSantra AWeigel RLurz F(2024)Lightweight and Person-Independent Radar-Based Hand Gesture Recognition for Classification and Regression of Continuous GesturesIEEE Internet of Things Journal10.1109/JIOT.2023.334730811:9(15285-15298)Online publication date: 1-May-2024
https://doi.org/10.1109/JIOT.2023.3347308
Zhang BZhou ZJiang BZheng R(2024)SUPER: Seated Upper Body Pose Estimation using mmWave Radars2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI61053.2024.00020(181-191)Online publication date: 13-May-2024
https://doi.org/10.1109/IoTDI61053.2024.00020
Akbar ASheng ZZhang QWang D(2023)Cross-Domain Gesture Sequence Recognition for Two-Player Exergames using COTS mmWave RadarProceedings of the ACM on Human-Computer Interaction10.1145/36264777:ISS(327-356)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1145/3626477
Zhao RYu JZhao HNgai E(2023)Radio2TextProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108737:3(1-28)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610873
Cui KYang QZheng YHan J(2023)mmRipple: Communicating with mmWave Radars through Smartphone VibrationProceedings of the 22nd International Conference on Information Processing in Sensor Networks10.1145/3583120.3586956(149-162)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3583120.3586956
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten