<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462024000401773</article-id>
<article-id pub-id-type="doi">10.13053/cys-28-4-4809</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Classification of Fall Events in the Elderly Using a Thermal Sensor and Machine Learning Techniques]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz-Ramírez]]></surname>
<given-names><![CDATA[Arnoldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz-Escobar]]></surname>
<given-names><![CDATA[Julia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quintero-Rosas]]></surname>
<given-names><![CDATA[Verónica]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moncada-Sánchez]]></surname>
<given-names><![CDATA[Rosendo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Mexicali Departamento de Sistemas y Computación ]]></institution>
<addr-line><![CDATA[Mexicali ]]></addr-line>
<country>Mexico</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>28</volume>
<numero>4</numero>
<fpage>1773</fpage>
<lpage>1781</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462024000401773&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462024000401773&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462024000401773&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: As reported by the World Health Organization, falls constitute the second leading cause of unintentional injury death worldwide. Particularly, adults older than 60 years suffer the most significant number of fatal falls or serious injuries, with nearly 30% of individuals over 65 reporting at least one fall annually, a risk that increases with age. The anticipated growth in life expectancy and the resulting larger aging population accentuates the economic burden associated with falls. Consequently, identifying effective strategies for fall prevention and early detection in the elderly has become relevant. This study proposes a non-invasive fall detection system based on a thermal sensor and a supervised machine-learning algorithm. The experimental dataset, generated by students through simulations of both fall and non-fall events, included the recording of room temperatures using a thermal sensor, along with the associated data labeling. For fall event detection, we evaluated three well-known supervised machine learning models: a Support Vector Machine, a Random Forest, and a Convolutional Neural Network. The experimental results demonstrate that these models exhibit robust capabilities in distinguishing between falls and non-fall events, consistently achieving performances above 95% across various evaluation metrics.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[lderly care]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[sensor monitoring]]></kwd>
<kwd lng="en"><![CDATA[fall events]]></kwd>
</kwd-group>
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