The dynamics of muscle force generation is directly related
to the movement dynamics of the skeletal system. Thus, modelling muscle dynamics is important to fully understand the control of movement
in humans. Abnormal movements caused by neuromuscular diseases such
as stroke, Parkinson's disease, or multiple sclerosis to name a few have all
in common the presence of some abnormal muscle tone. Muscle tone can
be effectively represented via short-range stiffness. Since stiffness is difficult to measure in real time, it is convenient to use numerical models to
assess muscle stiffness as function of muscle dynamics. In this work, two
different implementations of the Hill-type muscle model are considered
to estimate the lower limb joint stiffness during running. The obtained
results are discussed to evaluate how the choices of muscle models affect the estimation of lower limb joint stiffness. We found that stiffness
estimates are strongly dependent on the adopted muscle model. We observed different magnitude and timing of the estimated stiffness time
profile with respect to each gait phase, as function of the model used.
Furthermore, the two models produced substantially different joint stiffness time profiles for the ankle joint
