Generating vertical ground reaction forces using a stochastic data-driven model for pedestrian walking

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. • A stochastic model for the vertical GRFs while walking is presented. • The steps are normalized and modeled through a multivariate normal distribution. • Step scaling and double support times are also modeled as stochastic variables. • The obtained model is useful to reproduce GRFs similar to the experimental ones. • The model can be adapted to other near periodic human locomotion activities.