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.
