<?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>S0188-95322023000400128</article-id>
<article-id pub-id-type="doi">10.17488/rmib.44.4.9</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicación de Redes Neuronales Artificiales para la Clasificación de Actividades de la Vida Diaria en Sujetos con Enfermedad de Párkinson]]></article-title>
<article-title xml:lang="en"><![CDATA[Classification of Daily Living Activities in subjects with Parkinson&#8217;s Disease using Artificial Neural Networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodriguez Montero]]></surname>
<given-names><![CDATA[Lizeth]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ambrosio Bastián]]></surname>
<given-names><![CDATA[Jose]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Sanpablo]]></surname>
<given-names><![CDATA[Alberto Isaac]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad La Salle  ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra  ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>spe1</numero>
<fpage>128</fpage>
<lpage>139</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322023000400128&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322023000400128&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322023000400128&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El presente trabajo es un seguimiento a la propuesta para la contribución con especialistas en la salud para enriquecer los sistemas de seguimiento y apoyo en pacientes con Enfermedad de Párkinson a través de la clasificación de actividades de la vida diaria (AVDs) utilizando Redes Neuronales Artificiales programadas en lenguaje Python. El método propuesto de aprendizaje supervisado permitió la clasificación de 6 AVDs mediante 22 señales procedentes de haber aplicado Análisis de Componentes Principales; conformando la base de datos utilizada para entrenar un Perceptrón Multicapa, logrando un acercamiento a la clasificación con el 93% de medida F1-score. El presente estudio demuestra la versatilidad de las RNA basadas en MLP combinadas con la técnica de PCA, pues incluso en una base de datos desbalanceada como la utilizada permite alcanzar excelentes valores en la medida F1-score. El uso de Inteligencia Artificial y otras herramientas aplicadas en este trabajo pueden eventualmente ayudar a especialistas a desempeñar una evaluación más certera en el monitoreo de la rehabilitación en pacientes con enfermedad de Párkinson mejorando los registros y así evitar subjetividad en la interpretación de los resultados del tratamiento.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This paper is a proposal to contribute with health specialists to enrich the follow-up and support systems in patients with Parkinson's by identifying and classifying Daily Living Activities (DLAs) using Artificial Neural Networks programmed in Python language. The proposed method of supervised learning allowed the classification of 6 DLAs through 22 signals obtained from the application of Principal Component Analysis, creating a database used to train a Multilayer Perceptron. This model achieved an approximation of classification with 93% of the F1-score. The present study demonstrates the versatility of ANNs based on MLP combined with the PCA technique since, even in an unbalanced database such as the one used, it allows excellent values to be achieved in the F1-score measure. The use of Artificial Intelligence and other tools applied in this work may eventually help specialists to perform a more accurate assessment in the monitoring of rehabilitation for patients with Parkinson's disease by improving records and thus avoiding subjectivity in the interpretation of treatment results.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[enfermedad de Párkinson]]></kwd>
<kwd lng="es"><![CDATA[PCA]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[Parkinson&#8217;s disease]]></kwd>
<kwd lng="en"><![CDATA[PCA]]></kwd>
</kwd-group>
</article-meta>
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