2026-05-05T20:56:39Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/762842025-07-08T19:04:03Zcom_10324_966com_10324_952com_10324_894col_10324_967

2025-07-08T07:27:30Z urn:hdl:10324/76284 Generating vertical ground reaction forces using a stochastic data-driven model for pedestrian walking Magdaleno González, Álvaro García Terán, José María Pelaez Rodríguez, César Fernández Ordóñez, Guillermo Lorenzana Ibán, Antolín Producción Científica A novel time-domain approach to the characterization of the forces induced by a pedestrian is proposed. It focuses on the vertical component while walking, but thanks to how it is conceived, the algorithm can be easily adapted to other activities or any other force component. The work has been developed from the statistical point of view, so a stochastic data-driven model is finally obtained after the algorithm is applied to a set of experimentally measured steps. The model is composed of two mean vectors and their corresponding covariance matrices to represent the steps, as well as some more means and standard deviations to account for the step scaling and double support phase, under the assumption that the random variables follow normal distributions. Velocity and step length are also provided, so the model and the latter data enable the realistic generation of virtual gaits. Some application examples at different walking paces are shown, in which comparisons between the original steps and a set of virtual ones are performed to show the similarities between both. For reproducibility purposes, the data and the developed algorithm have been made available 2025-07-08T07:27:30Z 2025-07-08T07:27:30Z 2025 info:eu-repo/semantics/article Journal of Computational Science, 2025, vol. 88, p. 102602 1877-7503 https://uvadoc.uva.es/handle/10324/76284 10.1016/j.jocs.2025.102602 102602 Journal of Computational Science 88 eng https://www.sciencedirect.com/science/article/pii/S1877750325000791 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2025 The Author(s) Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier