PerKMP: Periodic Kernelized Movement Primitives for the Lower Limb Exoskeleton Real-Time Control and Transparency Enhancement

In the context of exoskeleton rehabilitation for patients in the later stages of recovery, reducing unnecessary interactive torque and enhancing exoskeleton transparency is important, where patients are encouraged to engage in voluntary movement. This study introduces a novel method known as Periodic Kernelized Movement Primitives (PerKMP) to address this issue. PerKMP is an advanced iteration of the original Kernelized Movement Primitives (KMP) that integrates periodic kernel functions for heightened adaptability. Two distinct PerKMP modules have been developed: the desired trajectory modulation PerKMP, which dynamically plans the desired trajectory according to the walking state, and the joint torque estimation PerKMP, which accurately estimates total joint torque in real-time and adjusts controller stiffness accordingly. A series of experiments with different walking speeds and different subjects were conducted to verify the validity of the method. Through the combined implementation of two PerKMP modules, the average absolute value of the interactive torque and energy per unit distance (EPUD) were reduced effectively. This research broadens the application of imitation learning methods in exoskeletons, and enables real-time control adjustments, thereby presenting a pioneering approach to enhancing the adaptability of exoskeleton rehabilitation systems.