<?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>2011-2084</journal-id>
<journal-title><![CDATA[International Journal of Psychological Research]]></journal-title>
<abbrev-journal-title><![CDATA[int.j.psychol.res.]]></abbrev-journal-title>
<issn>2011-2084</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Psicología. Universidad de San Buenaventura, Medellín]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2011-20842024000200084</article-id>
<article-id pub-id-type="doi">10.21500/20112084.7405</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A Volumetric Deep Architecture to Discriminate Parkinsonian Patterns from Intermediate Pose Representations]]></article-title>
<article-title xml:lang="es"><![CDATA[Una arquitectura volumétrica profunda para discriminar patrones parkinsonianos desde representaciones de poses intermedias]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Portilla]]></surname>
<given-names><![CDATA[Jean]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rangel]]></surname>
<given-names><![CDATA[Edgar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guayacán]]></surname>
<given-names><![CDATA[Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez]]></surname>
<given-names><![CDATA[Fabio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Industrial de Santander Vision and Learning Laboratory BIVL2ab- Biomedical Imaging]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>17</volume>
<numero>2</numero>
<fpage>84</fpage>
<lpage>90</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S2011-20842024000200084&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S2011-20842024000200084&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S2011-20842024000200084&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Parkinson&#8217;s disease (PD) is a common neurodegenerative disorder worldwide, with over 6.2 million registered cases. Gait analysis plays a fundamental role in evaluating motor abnormalities associated with this disease. However, current methods, such as marker-based systems, are intrusive and expert-dependent. Markerless alternatives, like video sequence analysis, have been proposed, but they tend to provide overall classification scores and lack the ability to interpret joint kinematics in detail. An innovative technique is presented using volumetric convolutional networks that can learn intermediate postural patterns and distinguish between Parkinson&#8217;s patients and control subjects. This approach utilizes OpenPose activations and then applies hierarchical convolution to minimize classification. In tests conducted with 14 Parkinson&#8217;s patients and 16 control subjects, this method achieved a classification accuracy of 98%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La enfermedad de Parkinson (EP) es un trastorno neurodegenerativo común a nivel mundial, con más de 6.2 millones de casos registrados. El análisis de la marcha desempeña un papel fundamental en la evaluación de las anomalías motoras asociadas con esta enfermedad. Sin embargo, los métodos actuales, como sistemas basados en marcadores, son intrusivos y dependientes de expertos. Se han propuesto alternativas sin marcadores, como el análisis de secuencias de video, que tienden a proporcionar puntajes de clasificación globales y carecen de la capacidad de interpretar la cinemática articular detalladamente. Se presenta una técnica innovadora utilizando redes convolucionales volumétricas que pueden aprender patrones posturales intermedios y distinguir entre pacientes con Parkinson y sujetos control. Este enfoque utiliza activaciones de OpenPose, y luego aplica una convolución jerárquica para minimizar la clasificación. En pruebas realizadas con 14 pacientes Parkinson y 16 sujetos control, este método alcanzó una precisión del 98% en clasificación.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Parkinson&#8217;s Disease]]></kwd>
<kwd lng="en"><![CDATA[Posture]]></kwd>
<kwd lng="en"><![CDATA[Artificial Neural Networks]]></kwd>
<kwd lng="en"><![CDATA[Gait]]></kwd>
<kwd lng="es"><![CDATA[Enfermedad de Parkinson]]></kwd>
<kwd lng="es"><![CDATA[postura]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[marcha]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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