A Practical and Adaptive Method to Achieve EMG-Based Torque Estimation for a Robotic Exoskeleton

To improve the efficacy of robotic exoskeleton-based rehabilitation training, active joint torque of subjects should be detected. This paper presents a practical and adaptive method to estimate active joint torque using electromyography (EMG) signals for a custom lower limb robotic exoskeleton with two degrees of freedom (DOFs). This estimator, constructed of radial basis function neural networks (RBFNNs), was used to form an extended Slotine-Li controller. This extended controller eliminated the need for the calibration for EMG-torque model. The adaptive control of exoskeleton and adaptive estimation of active joint torque were performed within the same framework. By introducing a two-step learning strategy into the controller, the estimator can continuously adapt to changes in the EMG-torque model, and overcome the problems due to the time-varying property of EMG signals. Simulation and experimental results show that the presented estimator can predict the active joint torque of subjects in a practical and adaptive manner. Additionally, the accurate movement control of exoskeleton is also guaranteed. At present, the experiments are conducted only for the swing phase due to the lack of the force plate sensors.