<?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>1692-3324</journal-id>
<journal-title><![CDATA[Revista Ingenierías Universidad de Medellín]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. ing. univ. Medellín]]></abbrev-journal-title>
<issn>1692-3324</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Medellín]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1692-33242024000100003</article-id>
<article-id pub-id-type="doi">10.22395/rium.v23n44a3</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[RECONOCIMIENTO DE TÉCNICAS OFENSIVAS EN ARTES MARCIALES: UN MAPEO SISTEMÁTICO]]></article-title>
<article-title xml:lang="en"><![CDATA[RECOGNITION OF OFFENSIVE TECHNIQUES IN MARTIAL ARTS: A SYSTEMATIC MAPPING STUDY]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cristobal Franco]]></surname>
<given-names><![CDATA[Jairo Josue]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguileta Güemez]]></surname>
<given-names><![CDATA[Antonio Armando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moo Mena]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Reyes Magaña]]></surname>
<given-names><![CDATA[Jorge Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Yucatán Facultad de Matemáticas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Yucatán Facultad de Matemáticas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma de Yucatán Facultad de Matemáticas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Autónoma de Yucatán Facultad de Matemáticas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<volume>23</volume>
<numero>44</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1692-33242024000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1692-33242024000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1692-33242024000100003&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Motivación: la identificación precisa de golpes y patadas en competencias deportivas de artes marciales es un asunto crítico, a menudo complicado y, en ocasiones, sujeto a controversias debido a la apreciación subjetiva de los árbitros. Problema: la subjetividad en la evaluación de golpes y patadas durante las competencias deportivas de artes marciales plantea un desafío significativo en términos de imparcialidad y precisión en el arbitraje. Enfoque de solución: este estudio se centra en el análisis de las contribuciones más recientes en el campo del reconocimiento de golpes y patadas en competencias de artes marciales. Se revisan técnicas de clasificación y sensores comúnmente utilizados. Resultados: el análisis proporciona una visión general de las técnicas de clasificación implementadas en el reconocimiento de golpes y patadas. Esto contribuye a la comprensión de los avances recientes en este campo y cómo pueden mejorar la objetividad y precisión en el arbitraje de las competencias de artes marciales. Conclusiones: este estudio destaca el creciente interés en técnicas de aprendizaje automático para clasificar golpes y patadas en artes marciales, abarcando una amplia gama de clasificadores, desde métodos tradicionales hasta modelos de aprendizaje profundo. La combinación de sensores inerciales y cámaras profundas se presenta como una vía prometedora. Se anticipa que futuras investigaciones compararán y caracterizarán exhaustivamente estos enfoques, allanando el camino para la implementación de sistemas de inteligencia artificial en competencias de artes marciales, lo que podría revolucionar la objetividad en la evaluación de movimientos en este deporte.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Motivation: The precise identification of punches and kicks in martial arts sporting competitions is a critical issue, often complex, and at times subject to controversies due to the subjective judgment of referees. Problem: The subjectivity in assessing punches and kicks during martial arts sporting competitions poses a significant challenge regarding impartiality and accuracy in refereeing. Solution Approach: This study analyzes the most recent contributions to punch and kick recognition in martial arts competitions. It reviews classification techniques, commonly used sensors, and the performance achieved in identifying these movements. Results: The analysis provides a general overview of implemented punch and kick classification techniques. This contributes to understanding recent advancements in this field and how they can enhance objectivity and precision in refereeing martial arts competitions. Conclusions: This study underscores the growing interest in machine learning techniques for classifying punches and kicks in martial arts, encompassing a wide range of classifiers, from traditional methods to deep learning models. The combination of inertial sensors and depth cameras emerges as a promising avenue. Future research is expected to thoroughly compare and characterize these approaches, paving the way for implementing artificial intelligence systems in martial arts competitions and potentially revolutionizing the objectivity in assessing movements in this sport.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[reconocimiento de actividades]]></kwd>
<kwd lng="es"><![CDATA[artes marciales]]></kwd>
<kwd lng="es"><![CDATA[taekwondo]]></kwd>
<kwd lng="es"><![CDATA[patadas]]></kwd>
<kwd lng="es"><![CDATA[sensores]]></kwd>
<kwd lng="es"><![CDATA[clasificadores]]></kwd>
<kwd lng="es"><![CDATA[mapeo sistemático]]></kwd>
<kwd lng="en"><![CDATA[activity recognition]]></kwd>
<kwd lng="en"><![CDATA[martial arts]]></kwd>
<kwd lng="en"><![CDATA[taekwondo]]></kwd>
<kwd lng="en"><![CDATA[kicking]]></kwd>
<kwd lng="en"><![CDATA[sensors]]></kwd>
<kwd lng="en"><![CDATA[classifiers]]></kwd>
<kwd lng="en"><![CDATA[systematic mapping]]></kwd>
</kwd-group>
</article-meta>
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