Trajectory generation and control of a knee exoskeleton based on dynamic movement primitives for sit-to-stand assistance

This paper presents a novel control approach for a knee exoskeleton to assist individuals with lower extremity weakness during sit-to-stand motion. The proposed method consists of a trajectory generator and an impedance controller. The trajectory generator uses a library of sample trajectories as the training data and the initial joint angles as the input to predict the user’s intended sit-to-stand trajectory. Utilizing the dynamic movement primitives theory, the trajectory generator represents the predicted trajectory in a time-normalized and rather a flexible framework. The impedance controller is then employed to provide assistance by guiding the knee joint to move along the predicted trajectory. Moreover, the human-exoskeleton interaction force is used as the feedback for on-line adaptation of the trajectory speed. The proposed control strategy was tested on a healthy adult who wore the knee exoskeleton on his leg. The subject was asked to perform a number of sit-to-stand movements from different sitting positions. Next, the measured data and the inverse dynamic model of the human-exoskeleton system are used to calculate the knee power and torque profiles. The results reveal that average muscle activity decreases when the subject is assisted by the exoskeleton.