<?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>S0188-95322017000300602</article-id>
<article-id pub-id-type="doi">10.17488/rmib.38.3.8</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Desarrollo de una Prótesis para Desarticulado de Muñeca Controlada por Señales de Electromiografía]]></article-title>
<article-title xml:lang="en"><![CDATA[Development of a Myoelectric-Controlled Prosthesis for Transradial Amputees]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez-García]]></surname>
<given-names><![CDATA[M. E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Dorantes-Méndez]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mendoza Gutiérrez]]></surname>
<given-names><![CDATA[M. O.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de San Luis Potosí  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2017</year>
</pub-date>
<volume>38</volume>
<numero>3</numero>
<fpage>602</fpage>
<lpage>620</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322017000300602&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322017000300602&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322017000300602&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN En este trabajo se presenta el desarrollo y puesta en operación de una prótesis robótica para pacientes amputados con desarticulado de muñeca. Esta prótesis consiste en un prototipo de impresión 3D que tiene dos grados de libertad que permiten realizar tareas de sujeción de tipo pinza, así como la orientación de objetos mediante los movimientos de pronación y supinación. Para el control de la prótesis se utilizan dos clasificadores de manera independiente: un clasificador bayesiano implementado en la plataforma Arduino y una red neuronal artificial implementada en el software MATLAB®; ambos realizan la clasificación de los movimientos mediante la adquisición, procesamiento y extracción de índices característicos de la señal de electromiografía. El clasificador bayesiano y la red neuronal artificial obtuvieron, respectivamente, una eficiencia de 97% y 100%, lo que muestra que los índices característicos seleccionados son adecuados para realizar la clasificación de señales de electromiografía propuesta. Se logró la creación de una prótesis mioeléctrica completamente funcional que, al ser elaborada con tecnología de impresión 3D, representa una alternativa de bajo costo a aquellas ofrecidas actualmente en el mercado.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT In this paper, the development and operation of a robotic prosthesis for transradial amputees is presented. This prosthesis consists in a 3D-printed prototype with two degrees of freedom, allowing the user to perform grip tasks and to orientate objects through pronation and supination movements. Two classifiers were used independently to control the prosthesis: a bayesian classifier implemented in an Arduino device and an artificial neural network implemented in MATLAB® software; both classify movements through the acquisition, processing and extraction of features from the electromyography signal. The bayesian classifier and the artificial neural network achieved an efficiency of 97% and 100%, respectively, which shows that the extracted features were suitable for the proposed electromyography classification. A completely functional 3D-printed myoelectric prosthesis was achieved, and it represents a low-cost alternative to those existent in the current market.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Prótesis]]></kwd>
<kwd lng="es"><![CDATA[Electromiografía]]></kwd>
<kwd lng="es"><![CDATA[Clasificador Bayesiano]]></kwd>
<kwd lng="es"><![CDATA[Red Neuronal Artificial]]></kwd>
<kwd lng="en"><![CDATA[Prosthesis]]></kwd>
<kwd lng="en"><![CDATA[Electromyography]]></kwd>
<kwd lng="en"><![CDATA[Bayesian Classifier]]></kwd>
<kwd lng="en"><![CDATA[Artificial Neural Network]]></kwd>
</kwd-group>
</article-meta>
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</ref-list>
</back>
</article>
