Data from: Step-to-step variations in human running reveal how humans run without falling
Srinivasan, Manoj; Seethapathi, Nidhi (2019), Data from: Step-to-step variations in human running reveal how humans run without falling, Dryad, Dataset, https://doi.org/10.5061/dryad.1nt24m0
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.
National Science Foundation, Award: 1254842