Abstract
In this paper, we propose a method for the automatic compliance evaluation of the hand washing procedure performed by healthcare providers. The ideal cleaning procedure, as defined by the guidelines of the World Health Organization (WHO), is split into a sequence of ten distinct and specific hand gestures which have to be performed in the proper order. Thus, the conformance verification problem is formulated as the problem of recognizing that at a given time instant a specific gesture is carried out by the subject. The considered recognition problem is faced through a deep neural network inspired to AlexNet that classifies each image providing as output the guessed gesture class that the subject is performing. Images are captured by a depth camera mounted in a top-view position. The performance of the proposed approach has been assessed on a brand new dataset of about 131.765 frames obtained from 74 continuous recording from trained personnel. Preliminary evaluation confirms the feasibility of the approach with a recognition rate at the frame level that is about \(77\%\), and is about \(98\%\) when using a mobile window of 1 s. The developed system will be deployed for training students of the medicine course on the surgical hand-washing procedure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asadi-Aghbolaghi, M., et al.: A survey on deep learning based approaches for action and gesture recognition in image sequences. In: IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp. 476–483, May 2017. https://doi.org/10.1109/FG.2017.150
Centers for Disease Control and Prevention: Hand hygiene in healthcare settings (2018). https://www.cdc.gov/handhygiene/
Chen, L., Wang, F., Deng, H., Ji, K.: A survey on hand gesture recognition. In: International Conference on Computer Sciences and Applications, pp. 313–316, December 2013. https://doi.org/10.1109/CSA.2013.79
Cheng, H., Yang, L., Liu, Z.: Survey on 3D hand gesture recognition. IEEE Trans. Circuits Syst. Video Technol. 26(9), 1659–1673 (2016). https://doi.org/10.1109/TCSVT.2015.2469551
Edmond, M., Goodell, A., Zuelzer, W., Sanogo, K., Elam, K., Bearman, G.: Successful use of alcohol sensor technology to monitor and report hand hygiene compliance. J. Hosp. Infect. (2010). https://doi.org/10.1016/j.jhin.2010.07.006
Foggia, P., Percannella, G., Saggese, A., Vento, M.: Recognizing human actions by a bag of visual words. In: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, pp. 2910–2915 (2013). https://doi.org/10.1109/SMC.2013.496
Haas, J., Larson, E.: Measurement of compliance with hand hygiene. J. Hosp. Infect. (2007). https://doi.org/10.1016/j.jhin.2006.11.013
Kaur, H., Rani, J.: A review: Study of various techniques of hand gesture recognition. In: IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–5, July 2016. https://doi.org/10.1109/ICPEICES.2016.7853514
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)
Marra, A.R., et al.: The use of real-time feedback via wireless technology to improve hand hygiene compliance. Am. J. Infect. Control. 42(6), 608–611 (2014). https://doi.org/10.1016/j.ajic.2014.02.006
Sonkusare, J.S., Chopade, N.B., Sor, R., Tade, S.L.: A review on hand gesture recognition system. In: IEEE International Conference on Computing Communication Control and Automation, pp. 790–794, February 2015. https://doi.org/10.1109/ICCUBEA.2015.158
Stilo, A., et al.: Hand washing in operating room: a procedural comparison. Epidemiol. Biostat. Public Health (2016)
Szilágyi, L., Lehotsky, A., Nagy, M., Haidegger, T., Benyó, B., Benyo, Z.: Stery-hand: A new device to support hand disinfection. In: International Conference of the IEEE Engineering in Medicine and Biology, pp. 4756–4759, August 2010. https://doi.org/10.1109/IEMBS.2010.5626377
WHO: Surgical hand preparation: state-of-the-art. In: World Health Organization (ed.) WHO Guidelines on Hand Hygiene in Health Care: First Global Patient Safety Challenge Clean Care Is Safer Care, chap. 13, pp. 54–60. WHO, Genevra (2009), https://www.ncbi.nlm.nih.gov/books/NBK144036/
World Health Organization: WHO guidelines on hand hygiene in health care (2009)
Zhang, P., White, J., Schmidt, D., Dennis, T.: Applying machine learning methods to predict hand hygiene compliance characteristics. In: IEEE EMBS International Conference on Biomedical Health Informatics (BHI), pp. 353–356, February 2017. https://doi.org/10.1109/BHI.2017.7897278
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Greco, L., Percannella, G., Ritrovato, P., Saggese, A., Vento, M. (2019). A System for Controlling How Carefully Surgeons Are Cleaning Their Hands. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-29891-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29890-6
Online ISBN: 978-3-030-29891-3
eBook Packages: Computer ScienceComputer Science (R0)