A Biomimetic Human-Robot Interface for Natural Cooperative Ankle Movement in Healthy and Post-Stroke Subjects

Although movement efficiency and smoothness are fundamental optimality principles in human motor control, few human-robot interfaces effectively integrate these two principles during human-robot interaction. To address this, we proposed a biomimetic human-robot interface (BHRI) by cascading an EMG-driven neuromechanical model with a minimum-jerk motor behavior model. To decode continuous motion intentions, a neuromechanical model was established to estimate voluntary ankle torque based on EMG signals. The voluntary torque then served as the input for a motor behavior model, which generated robotic motion based on the minimum-jerk principle. We conducted robot voluntary control experiments among five healthy and five post-stroke subjects to validate the BHRI in an ankle rehabilitation robot. For comparison with the BHRI, an interaction torque-based minimum-jerk controller (IMC) and an EMG-driven admittance controller (EAM) were also implemented. Compared to IMC, the results demonstrated that our BHRI significantly reduced the interaction torque (by 40.7% and 25.0%), EPUD (by 55.9% and 51.1%), and averaged muscular effort (by 8.4% and 13.3%) for healthy and post-stroke subjects, respectively. Furthermore, when compared with the EAM, our BHRI achieved a significant reduction in the DSJ value of the generated desired trajectory (by 32.7% and 40.0%) for healthy and post-stroke subjects, respectively. For the DSJ of actual trajectory, the BHRI significantly reduced the DSJ value by 34.3% for post-stroke subjects compared to EAM. Therefore, our BHRI offers a biomimetic solution for natural human-robot cooperation by jointly optimizing movement efficiency and smoothness.