Iterative learning control of functional electrical stimulation in the presence of voluntary user effort

Abstract

Worldwide 17 million people are left with impairment to their upper or lower limb following stroke. Functional electrical stimulation (FES) is a method of artificially activating muscles using electrical pulses and is the most common rehabilitation technology. A significant body of clinical research confirms that successful rehabilitation requires FES to be applied in a way that supports voluntary intention during repeated attempts at functional tasks. Electromyography (EMG) measures the voluntary contraction of muscles and has been used to directly control FES in openloop, however it is limited by poor accuracy. On the other hand, model-based feedback control can provide high accuracy, but does not explicitly promote voluntary intention.

A new dynamic model of the muscle activation, generated by combined voluntary nerve signals and FES, is developed in this paper. It includes both nonlinear recruitment and linear activation dynamics. An efficient identification procedure is then formulated which can be applied to people with stroke. A model-based hybrid EMG/FES control scheme is then derived based on the model structure, allowing tracking and volitional intention support to be simultaneously optimised for the first time. Exploiting the repeated nature of rehabilitation, the control framework is then extended to further improve tracking accuracy. That is achieved by learning from experience through iterative learning control. The framework is experimentally tested with results confirming it can deliver greater performance compared to existing FES approaches, which do not consider voluntary action in the model or controller.

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