Clasificación basada en aprendizaje automático de técnicas de empuje en patinaje de velocidad
DOI:
https://doi.org/10.37135/ns.01.18.08Palabras clave:
Biomecánica, Red neuronal convolucional, Aprendizaje automático, OpenPose, Patinaje de velocidadResumen
El patinaje de velocidad es un deporte prestigioso que requiere habilidades técnicas y una condición física óptima. Durante el entrenamiento, las técnicas de visión artificial y aprendizaje automático (ML) pueden contribuir a mejorar el rendimiento en el patinaje y el análisis biomecánico. Sin embargo, la investigación sobre la clasificación automatizada de las técnicas de empuje en el patinaje de velocidad mediante métodos de estimación de la postura es limitada. En este sentido, se propone el modelo OpenPose para obtener datos de las articulaciones y puntos clave del patinador para el análisis del movimiento y la clasificación del empuje. La metodología de clasificación implicó explorar dos enfoques principales. El primer enfoque utiliza la clasificación de imágenes mediante imágenes de energía de la marcha del esqueleto (SGEI) y una red neuronal convolucional (CNN) basada en la arquitectura VGG19 con aprendizaje por transferencia, logrando una precisión del 90,72 %. El segundo enfoque utiliza vectores de características biomecánicas mediante un sistema de máquina de vectores de soporte (SVM) y un algoritmo de bosque aleatorio (RF), logrando precisiones del 94 % y el 92 %, respectivamente. La tarea de clasificación consideró tres técnicas de impulso en patinaje: impulso clásico, impulso doble e impulso pendular, las cuales son biomecánicamente relevantes debido a su influencia en la eficiencia de la propulsión, el equilibrio y el rendimiento en patinaje. Los hallazgos clave indican que los modelos basados en características (SVM/RF) lograron mayor precisión y una ejecución más rápida, mientras que el enfoque CNN proporcionó mayor flexibilidad a través del aumento de datos y el ajuste automático de parámetros. Además, la técnica de "impulso doble" fue el movimiento clasificado con mayor precisión en todos los modelos evaluados. El marco propuesto contribuye a la escasa investigación sobre la clasificación automatizada del impulso en patinaje mediante la estimación de la pose y técnicas de aprendizaje automático para el análisis biomecánico y del rendimiento deportivo.
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