Advanced methods for control of neural prostheses

Neural prosthesis based on functional electrical stimulation can restore movement in individuals with paralysis caused by central nervous system injury. Rule based control (RBC) is a promising approach for the control of a neural prosthesis for movement restoration. We present a method for the design of RBC for real time control of walking. For the design of control data from embedded sensors system are used as inputs and muscle activation profiles derived the optimal control simulation as outputs. This is a two step procedure: 1.) The input-output data for machine learning (ML) are generated using biomechanical gait simulations., 2.) The rules are determined by applying ML based on artificial neural network. The controller is trained and evaluated using the data recorded from an able bodied subject walking at two gait speeds. Results showed that the estimation of muscle activations was satisfactory at the gait speed for which the controller was trained. Moreover, the RBC demonstrated the ability to generalize to the gait speed that was higher/lower then the one actually used for the training.