<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322021000200109</article-id>
<article-id pub-id-type="doi">10.17488/rmib.42.2.13</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Autonomic Face Mask Detection with Deep Learning: an IoT Application]]></article-title>
<article-title xml:lang="es"><![CDATA[Detección Autonómica de Cubrebocas con Aprendizaje Profundo: una Aplicación del IoT]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Benitez-Baltazar]]></surname>
<given-names><![CDATA[V. H.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pacheco-Ramírez]]></surname>
<given-names><![CDATA[J. H.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moreno-Ruiz]]></surname>
<given-names><![CDATA[J. R.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nuñez-Gurrola]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Sonora  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2021</year>
</pub-date>
<volume>42</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322021000200109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322021000200109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322021000200109&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 4 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Un virus nuevo y letal conocido como SARS-CoV-2, responsable de la enfermedad del coronavirus (COVID-19), se está propagando rápidamente por el mundo y ha provocado más de 4 millones de muertes. Por tal razón, existe una necesidad urgente de encontrar formas nuevas e innovadoras de reducir la probabilidad de infección. Una de las formas usuales de contraer el virus es al estar en contacto con las gotas de saliva de una persona enferma. Este riesgo se puede reducir usando una mascarilla tipo cubrebocas como sugiere la Organización Mundial de la Salud (OMS), especialmente en entornos cerrados como aulas, hospitales y supermercados. Sin embargo, las personas dudan en usar una mascarilla, lo que aumenta el riesgo de propagar la enfermedad, además, cuando se usa la mascarilla, a veces se usa de manera incorrecta. En este trabajo de investigación se propone un sistema autonómico de detección de mascarilla con aprendizaje profundo empoderado con la técnica de detección de imágenes que se utiliza en desarrollos de realidad aumentada como mecanismo para solicitar el correcto uso de mascarilla para permitir el acceso de personas a zonas críticas. Para lograr esto, se construyó un modelo de aprendizaje máquina basado en redes neuronales convolucionales con un enfoque de IoT para hacer cumplir el uso correcto de la máscara facial en las áreas requeridas, tal como lo exige la ley en algunas regiones.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[COVID - 19]]></kwd>
<kwd lng="en"><![CDATA[Cyber-Physical Systems]]></kwd>
<kwd lng="en"><![CDATA[Internet of Things]]></kwd>
<kwd lng="es"><![CDATA[Machine learning]]></kwd>
<kwd lng="es"><![CDATA[COVID - 19]]></kwd>
<kwd lng="es"><![CDATA[Cyber-Physical Systems]]></kwd>
<kwd lng="es"><![CDATA[Internet of Things]]></kwd>
</kwd-group>
</article-meta>
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