A study on machine vision techniques for the inspection of health personnels' protective suits for the treatment of patients in extreme isolation

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dc.contributor.author Stazio, Alice
dc.contributor.author González Víctores, Juan Carlos
dc.contributor.author Estévez Fernández, David
dc.contributor.author Balaguer Bernaldo de Quirós, Carlos
dc.date.accessioned 2020-11-16T16:17:10Z
dc.date.available 2020-11-16T16:17:10Z
dc.date.issued 2019-06-30
dc.identifier.bibliographicCitation Stazio, A., Victores, J. G., Estevez, D., Balaguer, C. (2019). A Study on Machine Vision Techniques for the Inspection of Health Personnels’ Protective Suits for the Treatment of Patients in Extreme Isolation. Electronics, 8(7), 743
dc.identifier.issn 2079-9292
dc.identifier.uri http://hdl.handle.net/10016/31420
dc.description.abstract The examination of Personal Protective Equipment (PPE) to assure the complete integrity of health personnel in contact with infected patients is one of the most necessary tasks when treating patients affected by infectious diseases, such as Ebola. This work focuses on the study of machine vision techniques for the detection of possible defects on the PPE that could arise after contact with the aforementioned pathological patients. A preliminary study on the use of image classification algorithms to identify blood stains on PPE subsequent to the treatment of the infected patient is presented. To produce training data for these algorithms, a synthetic dataset was generated from a simulated model of a PPE suit with blood stains. Furthermore, the study proceeded with the utilization of images of the PPE with a physical emulation of blood stains, taken by a real prototype. The dataset reveals a great imbalance between positive and negative samples; therefore, all the selected classification algorithms are able to manage this kind of data. Classifiers range from Logistic Regression and Support Vector Machines, to bagging and boosting techniques such as Random Forest, Adaptive Boosting, Gradient Boosting and eXtreme Gradient Boosting. All these algorithms were evaluated on accuracy, precision, recall and F1 score; and additionally, execution times were considered. The obtained results report promising outcomes of all the classifiers, and, in particular Logistic Regression resulted to be the most suitable classification algorithm in terms of F1 score and execution time, considering both datasets.
dc.description.sponsorship The research leading to these results received funding from: Inspección robotizada de los trajes de proteccion del personal sanitario de pacientes en aislamiento de alto nivel, incluido el ébola, Programa Explora Ciencia, Ministerio de Ciencia, Innovación y Universidades (DPI2015-72015-EXP); the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; and ROBOESPAS: Active rehabilitation of patients with upper limb spasticity using collaborative robots, Ministerio de Economía, Industria y Competitividad, Programa Estatal de I+D+i Orientada a los Retos de la Sociedad (DPI2017-87562-C2-1-R).
dc.language.iso eng
dc.publisher MDPI
dc.rights Reconocimiento 3.0 España
dc.rights © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Personal protective equipment (PPE)
dc.subject.other Machine vision
dc.subject.other Class imbalance
dc.subject.other Synthetic dataset
dc.subject.other Physical emulation
dc.subject.other Adaboost
dc.subject.other Support vector machine (SVM)
dc.subject.other Infectious diseases
dc.subject.other Healthcare
dc.title A study on machine vision techniques for the inspection of health personnels' protective suits for the treatment of patients in extreme isolation
dc.type article
dc.type.review PeerReviewed
dc.subject.eciencia Robótica e Informática Industrial
dc.identifier.doi https://doi.org/10.3390/electronics8070743
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. DPI2017-87562-C2-1-R
dc.relation.projectID Gobierno de España. DPI2015-72015-EXP
dc.relation.projectID Comunidad de Madrid. S2018/NMT-4331/RoboCity2030-DIH-CM
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 7
dc.identifier.publicationlastpage 14
dc.identifier.publicationtitle Electronics
dc.identifier.publicationvolume 8
dc.identifier.uxxi AR/0000024963
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades (España)
dc.contributor.funder Ministerio de Economía, Industria y Competitividad (España)
dc.relation.dataset https://www.doi.org/10.5281/zenodo.3251898
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