Tele-impedance based assistive control for a compliant knee exoskeleton

• We propose a tele-impedance based assistive control for a knee exoskeleton.
• An EMG-driven biomechanical model estimates the net torque and joint stiffness trend index.
• The outputs of the model are used to derive the trajectory and stiffness of the exoskeleton’s impedance controller.
• The exoskeleton can generate assistive actions that are volitionally controlled by the user’s muscle activity.

This paper presents a tele-impedance based assistive control scheme for a knee exoskeleton device. The proposed controller captures the user’s intent to generate task-related assistive torques by means of the exoskeleton in different phases of the subject’s normal activity. To do so, a detailed musculoskeletal model of the human knee is developed and experimentally calibrated to best match the user’s kinematic and dynamic behavior. Three dominant antagonistic muscle pairs are used in our model, in which electromyography (EMG) signals are acquired, processed and used for the estimation of the knee joint torque, trajectory and the stiffness trend, in real time. The estimated stiffness trend is then scaled and mapped to a task-related stiffness interval to agree with the desired degree of assistance. The desired stiffness and equilibrium trajectories are then tracked by the exoskeleton’s impedance controller. As a consequence, while minimum muscular activity corresponds to low stiffness, i.e. highly transparent motion, higher co-contractions result in a stiffer joint and a greater level of assistance. To evaluate the robustness of the proposed technique, a study of the dynamics of the human–exoskeleton system is conducted, while the stability in the steady state and transient condition is investigated. In addition, experimental results of standing-up and sitting-down tasks are demonstrated to further investigate the capabilities of the controller. The results indicate that the compliant knee exoskeleton, incorporating the proposed tele-impedance controller, can effectively generate assistive actions that are volitionally and intuitively controlled by the user’s muscle activity.