Telerehabilitation with Exoskeletons using Adaptive Robust Integral RBF-Neural-Network Impedance Control under Variable Time Delays

With an unprecedented increase in the global aging population and with it, the age-related neuromuscular dysfunction diseases, there is an exorbitant and escalating need for physical rehabilitation. Delivering these services - especially to those that are most vulnerable - under the current COVID-19 pandemic restriction for physical-distancing, is an even greater challenge. Interest in telerehabilitation is spiking, and robotic tel-erehabilitation could drastically improve patients' access to care. Some of the major challenges in developing the control methods for these robots are identifying, estimating, and overcoming the effects of dynamic modeling uncertainties, nonlinearities, and disturbances. Having humans in the loop creates the additional need for safety and compliance. Telerehabilitation control methods have the added requirement of delivering telepresence and addressing communication delays which, if not managed, could result in ineffective therapy, destabilize the system, and even cause injury. In this paper, we present a novel adaptive robust integral Radial Basis Function Neural Network Impedance model (RBFNN-I) control method for telerehabilitation with robotic exoskeletons which compensates for dynamic modeling uncertainties in the presence of external human torques and time delays. One of the salient features of the proposed control system is the implementation of a new human torque regulator which improves telepresence. Stability proof using Lyapunov stability theory is shown for the proposed control method. An exoskeleton was designed and used for unilateral and bilateral telerehabilitation simulations. Excellent tracking performance, telepresence and stability was achieved in the presence of large, variable and asymmetric time delays and human torques.