<?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>2518-4431</journal-id>
<journal-title><![CDATA[Investigación & Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Inv. y Des.]]></abbrev-journal-title>
<issn>2518-4431</issn>
<publisher>
<publisher-name><![CDATA[UNIVERSIDAD PRIVADA BOLIVIANA]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2518-44312022000100109</article-id>
<article-id pub-id-type="doi">10.23881/idupbo.022.1-9i</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[SISTEMA DE MONITOREO REMOTO DE RESPIRACIÓN Y NIVELES DE GASES, PARA PACIENTES RECUPERADOS DE COVID-19]]></article-title>
<article-title xml:lang="en"><![CDATA[REMOTE MONITORING SYSTEM OF BREATHING AND GAS LEVELS, FOR PATIENTS RECOVERED FROM COVID-19]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Minaya Urquidi]]></surname>
<given-names><![CDATA[Arsenio Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García]]></surname>
<given-names><![CDATA[Juan Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villarroel-Beltrán]]></surname>
<given-names><![CDATA[Valentina Rita]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barrero Mendizábal]]></surname>
<given-names><![CDATA[Luis Marcel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tapia Siles]]></surname>
<given-names><![CDATA[Silvia Cecilia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Privada Boliviana Laboratorio de Innovación Tecnológica Industrial y Robótica (LITIR) ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Bolivia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2022</year>
</pub-date>
<volume>22</volume>
<numero>1</numero>
<fpage>109</fpage>
<lpage>123</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_arttext&amp;pid=S2518-44312022000100109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_abstract&amp;pid=S2518-44312022000100109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_pdf&amp;pid=S2518-44312022000100109&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN En este proyecto se presenta el desarrollo de una máscara capaz de medir la frecuencia respiratoria (FR) y datos relacionados a la calidad de aire en conjunto con una aplicación móvil que recopila la información, para luego ser almacenada en una base de datos. La información recolectada es procesada y entregada a médicos para el control y monitoreo de sus pacientes. Esto debido a que los pacientes recuperados de COVID-19 no suelen ser lo suficientemente sistemáticos para registrar la información solicitada por los médicos locales que los atienden. También se tomó en cuenta la baja disponibilidad de equipos especializados en el ámbito del monitoreo respiratorio. La FR se midió con un sensor de temperatura y un micrófono. Se utilizaron algoritmos de Transformada Rápida de Fourier y Machine Learning que juntos conformaron una Fusión de Sensores de nivel básico para tener una respuesta más confiable. Los resultados de las mediciones de FR obtenidas a partir de la fusión de sensores tienen una desviación estándar de 5.97 y una estimación exacta en el 67% de las pruebas. Debido a que los materiales son populares a nivel mundial, este proyecto es fácilmente replicable en países de bajos recursos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This project presents the development of a mask to measure the respiratory rate (RR) and data related to air quality, in conjunction with a mobile application that collects information and then stores it in a database. The information collected is processed and delivered to doctors for the control and monitoring of their patients. This is because recovered COVID-19 patients are usually, not systematic enough, to record the information requested by the local doctors who treat them. We also take into account the low availability of specialized equipment in the field of respiratory monitoring. RR was measured with a temperature sensor and a microphone. Fast Fourier Transform and Machine Learning algorithms were used which together formed an entry-level Sensor Fusion to have a more reliable response. The results of the RR measurements obtained from sensor fusion have a standard deviation of 5.97 and an exact estimate in 67% of the tests. Because the materials are popular globally, this project is easily replicable in low-resource countries.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Frecuencia Respiratoria]]></kwd>
<kwd lng="es"><![CDATA[Smart-Mask]]></kwd>
<kwd lng="es"><![CDATA[Calidad de Aire]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[FFT]]></kwd>
<kwd lng="es"><![CDATA[Machine Learning]]></kwd>
<kwd lng="es"><![CDATA[Fusión de Sensores]]></kwd>
<kwd lng="en"><![CDATA[Respiratory Rate]]></kwd>
<kwd lng="en"><![CDATA[Smart-Mask]]></kwd>
<kwd lng="en"><![CDATA[Air Quality]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[FFT]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[Sensor Fusion]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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