<?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>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[RCCI]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992022000200001</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Walking speed estimation based on the use of a single inertial measurement unit and an artificial neural network]]></article-title>
<article-title xml:lang="es"><![CDATA[Estimación de la velocidad de la marcha basada en una Unidad de Medición Inercial y una red neuronal]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arencibia Castellanos]]></surname>
<given-names><![CDATA[Gianna]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Montero]]></surname>
<given-names><![CDATA[Fidel Ernesto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Serrano Blanco]]></surname>
<given-names><![CDATA[Leisy]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aznielle Rodríguez]]></surname>
<given-names><![CDATA[Tania Yadira]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Cuban Center for Neuroscience  ]]></institution>
<addr-line><![CDATA[ Havana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Technological University of Havana  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>16</volume>
<numero>2</numero>
<fpage>1</fpage>
<lpage>14</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992022000200001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992022000200001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992022000200001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This work addresses the estimation of the walking speed by using an artificial neural network (ANN) and a single Inertial Measurement Unit (IMU) placed in the lumbar region, at L3 level. In this context, the main contribution resides in the new features proposed for accomplishing the task, which confer simplicity and effectiveness to the procedure. The ANN application was validated through a database comprising IMU signals recorded from the execution of gait tests on 23 subjects with ages between 20 to 50 years old. In this work, eleven different architectures of multilayer feed-forward ANN were studied in order to choose the most effective one. Cross validation procedure was implemented to assess the quality of the estimation through the computation of the root mean square error (RMSE), the absolute error and the relative error. The most effective model (5-5-1) exhibited a RMSE equal to 0.08 m/s, which is in the range of the results presented in similar studies in this field.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Este trabajo tiene como objetivo la estimación de la velocidad de la marcha usando una red neuronal artificial (ANN) y un único sensor inercial (IMU) localizado en la región lumbar, en el nivel L3. En este contexto, la principal contribución radica en la propuesta de nuevos rasgos característicos que le confieren más simplicidad y efectividad al procedimiento. La aplicación de ANN fue validada usando una base de datos formadas por señales registradas con un IMU durante la marcha de 23 sujetos con edades entre 20 y 50 años. En este trabajo, fueron estudiadas 11 arquitecturas diferentes de redes neuronales multicapas para seleccionar la más efectiva. La validación cruzada fue implementada para evaluar la calidad de la estimación usando los parámetros error cuadrático medio (RMSE), error absoluto y error relativo. El modelo más efectivo (5-5-1) mostró un RMSE igual a 0.08 m/s, el cual está en el rango de los resultados presentados en estudios similares en este campo.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial Neural Network]]></kwd>
<kwd lng="en"><![CDATA[IMU]]></kwd>
<kwd lng="en"><![CDATA[Walking speed]]></kwd>
<kwd lng="es"><![CDATA[Red Neuronal Artificial]]></kwd>
<kwd lng="es"><![CDATA[IMU]]></kwd>
<kwd lng="es"><![CDATA[Velocidad de la marcha]]></kwd>
</kwd-group>
</article-meta>
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