http://www.jneuroengrehab.com/content/10/1/73/abstract
Abstract (provisional)
Gait distortion is the first clinical manifestation of many pathological disorders.
Traditionally, the gait laboratory has been the only available tool for supporting
both diagnosis and prognosis, but under the limitation that any clinical interpretation
depends completely on the physician expertise. This work presents a novel human gait
model which fusions two important gait information sources: an estimated Center of
Gravity (CoG) trajectory and learned heel paths, by that means allowing to reproduce
kinematic normal and pathological patterns. The CoG trajectory is approximated with
a physical compass pendulum representation that has been extended by introducing energy
accumulator elements between the pendulum ends, thereby emulating the role of the
leg joints and obtaining a complete global gait description. Likewise, learned heel
paths captured from actual data are learned to improve the performance of the physical
model, while the most relevant joint trajectories are estimated using a classical
inverse kinematic rule. The model is compared with standard gait patterns, obtaining
a correlation coefficient of 0.96. Additionally,themodel simulates neuromuscular diseases
like Parkinson (phase 2, 3 and 4) and clinical signs like the Crouch gait, case in
which the averaged correlation coefficient is 0.92.
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