RT info:eu-repo/semantics/article
T1 A Reduced Stochastic Data-Driven Approach to Modelling and Generating Vertical Ground Reaction Forces During Running
A1 Fernández, Guillermo
A1 García-Terán, José María
A1 Iglesias-Pordomingo, Álvaro
A1 Peláez-Rodríguez, César
A1 Lorenzana, Antolin
A1 Magdaleno, Alvaro
K1 human loading; running forces model; stochastic data-driven model; reduced model; virtual GRFs
AB This work presents a time-domain approach for characterizing the Ground Reaction Forces(GRFs) exerted by a pedestrian during running. It is focused on the vertical component,but the methodology is adaptable to other components or activities. The approach isdeveloped from a statistical perspective. It relies on experimentally measured force-timeseries obtained from a healthy male pedestrian at eight step frequencies ranging from130 to 200 steps/min. These data are subsequently used to build a stochastic data-drivenmodel. The model is composed of multivariate normal distributions which represent thestep patterns of each foot independently, capturing potential disparities between them.Additional univariate normal distributions represent the step scaling and the aerial phase,the latter with both feet off the ground. A dimensionality reduction procedure is alsoimplemented to retain the essential geometric features of the steps using a sufficientset of random variables. This approach accounts for the intrinsic variability of runninggait by assuming normality in the variables, validated through state-of-the-art statisticaltests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables thegeneration of virtual GRFs using pseudo-random numbers from the normal distributions.Results demonstrate strong agreement between virtual and experimental data. The virtualtime signals reproduce the stochastic behavior, and their frequency content is also capturedwith deviations below 4.5%, most of them below 2%. This confirms that the methodeffectively models the inherent stochastic nature of running human gait.
PB MDPI
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/82106
UL https://uvadoc.uva.es/handle/10324/82106
LA spa
NO MDPI, 2025
NO Producción Científica
DS UVaDOC
RD 24-ene-2026