Implementación de métodos computacionales para estimar las amplitudes angulares de los miembros inferiores durante el squat
Resumen
En 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.
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