Design of a control framework for lower limb exoskeleton rehabilitation robot based on predictive assessment


Patients suffering from lower limb dyskinesia, especially in early stages of rehabilitation,
have weak residual muscle strength in affected limb and require passive training by
the lower limb rehabilitation robot. Anatomy indicates that the biceps femoris short
head muscle has a strong influence on knee motion at the swing phase of walking. We
sought to explore how it would influence on gait cycle in optimization framework.
However, the training trajectory of conventional rehabilitation robots performing
passive training usually follows gait planning based on general human gait data, which
cannot simultaneously ensure both effective rehabilitation of affected limbs with
varying severity pathological gait and comfort of the wearer within a safe motion


To elucidate the effects of weakness and contracture, we systematically introduced
isolated defects into the musculoskeletal model and generated walking simulations
to predict the gait adaptation due to these defects. An impedance control model of
the rehabilitation robot is developed. Knee joint parameters optimized by predictive
forward dynamics simulation are adopted as the expected values for the robot controller
to achieve customized adjustment of the robot motion trajectory.


Severe muscle contracture leads to severe knee flexion; severe muscle weakness induces
a significant posterior tilt of the upper trunk, which hinders walking speed.


Our simulation results attempt to reveal pathological gait features, which may help
to reproduce the simulation of pathological gait. Furthermore, the robot simulation
results show that the robot system achieves a speedy tracking by setting a larger
stiffness value. The model also allows the implementation of different levels of damping
or elasticity effects.

Trial registration: The method proposed in this paper is an initial basic study that did not reach clinical
trials and therefore retains retrospectively registered.

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