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 muscle using electrical pulses and is the most common rehabilitation technology. A significant body of clinical research confirms that successful rehabilitation require FES to be applied in a way that supports voluntary intention during repeated attempts at functional tasks. Electromyography (EMG) measures the voluntary contraction of muscle and has been used to directly control FES, thereby embedding this connection. However, existing controllers do not use feedback and are not model-based, and subsequently cannot assist people with stroke to accurately perform the required task. A new dynamic model of the muscle activation generated by combined voluntary nerve signals and FES is developed in this paper, and 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 optimized for the first time.
Exploiting the repeated nature of rehabilitation, the control framework is then extended to further improve tracking accuracy 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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