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.

With the rapid development of rehabilitation robotics, there is a pressing need for efficient and accurate gait prediction methods. However, due to the complexity and variability of individual gait characteristics and external disturbances, accurately predicting gait in real time remains a significant challenge. This paper proposes an innovative Bayesian-inference-based method for real-time gait prediction while a subject walks with a lower-limb exoskeleton. Periodic gait information is represented using von Mises basis functions, and the weight parameters serve as real-time updated state variables. The error-subspace transform Kalman filter (ESTKF) is applied for gait trajectory prediction. A fully connected neural network (FCNN) is used to estimate the walking speeds in real time based on predicted trajectories. Comparative experiments based on an open-source database prove the advantages of ESKTF compared with other Bayesian filters. Walking experiments are conducted to estimate phase and speed in real time, and to predict the joint angle, total joint torque, and lower-limb muscle surface electromyography (sEMG) values. Experimental results validate the method's prediction performance across different speeds and demonstrate its resilience to external interference.