Implementation of computational methods to estimate lower limb angle amplitudes during squat

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dc.creatorBlanco-Díaz, Cristian Felipe
dc.creatorGuerrero-Méndez, Cristian David
dc.creatorDuarte-González, Mario Enrique
dc.creatorJaramillo-Isaza, Sebastián
dc.date2022-03-08
dc.date.accessioned2025-10-01T23:52:46Z
dc.descriptionIn biomechanics, motion capture systems based on video and markers are the most widely used method to estimate kinematic parameters. However, from a technical standpoint, experimental errors in data capture are often related to the masking of markers during motion capture. This phenomenon generates data loss that can affect the analysis of the results. The lack of data is solved by increasing the number of cameras or using additional devices such as inertial sensors. However, those additions increase the experimental cost of this method. Nowadays, new computational methods can be used to solve such problems less expensively. This study implemented two computational methods based on Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) to estimate the amplitude of limb angles during the execution of a movement on a single axis (i.e., the z-axis). The characteristics of the squats were used to train and validate the models. The results obtained include RMSE values lower than 14 (minimum RMSE of 5.35) and CC values close to 0.98. The estimated values are very close to the experimental amplitude angles, and the statistical analyses showed no significant differences between the distributions and means of the estimated amplitude values and their actual counterparts (p-value>0.05). The results show that these methods could help biomechanics researchers perform accurate analyses, decrease the number of cameras needed, reduce uncertainty, and avoid data loss problems.en-US
dc.descriptionEn biomecánica, los sistemas de captura de movimiento basados en video y en marcadores son el método más utilizado para la estimación de parámetros cinemáticos. A nivel técnico, los errores experimentales en la captura de datos suelen estar relacionados con el ocultamiento de los marcadores durante la captura del movimiento. Este fenómeno genera una pérdida de datos que puede afectar el análisis de los resultados. La falta de datos se resuelve aumentando el número de cámaras o utilizando dispositivos adicionales como sensores inerciales. Estas adiciones incrementan el costo experimental de este método. Actualmente, para resolver este tipo de problemas de forma menos costosa, se podrían utilizar nuevos métodos computacionales. Este estudio tiene como objetivo implementar dos métodos computacionales basados en red neuronal artificial (RNA) y regresión de vectores de soporte (RVS) para estimar la amplitud del ángulo de las extremidades durante la ejecución de un movimiento a partir de un solo eje (eje Z). Para entrenar y validar los modelos, se utilizaron características del ejercicio de squat. Los resultados obtenidos incluyeron valores de raíces de error cuadrático medio (RMSE) inferiores a 14 (RMSE mínimo de 5.35) y valores de CC cercanos a 0.98. Los valores estimados son muy cercanos a los ángulos de amplitud experimentales, los análisis estadísticos muestran que no hay diferencias significativas entre las distribuciones y las medias de los valores de amplitud estimados y los valores reales (valor p>0.05). Los resultados demuestran que estos métodos podrían ayudar a los investigadores en biomecánica a realizar análisis precisos, reduciendo el número de cámaras necesarias, reduciendo la incertidumbre y evitando problemas por perdida de datos.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2164
dc.identifier10.22430/22565337.2164
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7807
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2164/2329
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2164/2331
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2164/2332
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2164/2333
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dc.rightsDerechos de autor 2022 TecnoLógicases-ES
dc.sourceTecnoLógicas; Vol. 25 No. 53 (2022); e2164en-US
dc.sourceTecnoLógicas; Vol. 25 Núm. 53 (2022); e2164es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectArtificial Neural Networksen-US
dc.subjectBiomechanical analysisen-US
dc.subjectSquat analysisen-US
dc.subjectComputational modeling in biomechanicsen-US
dc.subjectLower limb angle amplitudesen-US
dc.subjectRedes Neuronales Artificiales (RNA)es-ES
dc.subjectanálisis biomecánicoes-ES
dc.subjectanálisis de squates-ES
dc.subjectmodelado computacional en biomecánicaes-ES
dc.subjectamplitud angular de miembros inferioreses-ES
dc.titleImplementation of computational methods to estimate lower limb angle amplitudes during squaten-US
dc.titleImplementación de métodos computacionales para estimar las amplitudes angulares de los miembros inferiores durante el squates-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

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