Data from: Constructing predictive models of human running

Running is an essential mode of human locomotion, during which ballistic
aerial phases alternate with phases when a single foot contacts the
ground. The spring-loaded inverted pendulum (SLIP) provides a starting
point for modelling running, and generates ground reaction forces that
resemble those of the centre of mass (CoM) of a human runner. Here, we
show that while SLIP reproduces within-step kinematics of the CoM in three
dimensions, it fails to reproduce stability and predict future motions. We
construct SLIP control models using data-driven Floquet analysis, and show
how these models may be used to obtain predictive models of human running
with six additional states comprising the position and velocity of the
swing-leg ankle. Our methods are general, and may be applied to any
rhythmic physical system. We provide an approach for identifying an
event-driven linear controller that approximates an observed stabilization
strategy, and for producing a reduced-state model which closely recovers
the observed dynamics.