Data from: Step-to-step variations in human running reveal how humans run without falling

Humans can run without falling down, usually despite uneven terrain or
occasional pushes. Even without such external perturbations, intrinsic
sources like sensorimotor noise perturb the running motion incessantly,
making each step variable. Here, using simple and generalizable models, we
show that even such small step-to-step variability contains considerable
information about strategies used to run stably. Deviations in the center
of mass motion predict the corrective strategies during the next stance,
well in advance of foot touchdown. Horizontal motion is stabilized by
total leg impulse modulations, whereas the vertical motion is stabilized
by differentially modulating the impulse within stance. We implement these
human-derived control strategies on a simple computational biped, showing
that it runs stably for hundreds of steps despite incessant noise-like
perturbations or larger discrete perturbations. This running controller
derived from natural variability echoes behaviors observed in previous
animal and robot studies.