Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling

The consequences of a stroke is a major and increasing problem world wide. Many people who suffer a stroke are left with permanent impairment but the possibility exists that suitable rehabilitation could increase mobility and, for example, enable independent living. This, in turn, requires effective rehabilitation where it is known that currently available methods are relatively poor and are not well suited to home use, where the latter aspect is critical to improving practice of rehabilitation tasks and reducing costs. An accepted method to relearn lost function, such as reaching out to an object, is repeated attempts with learning from those already completed, supported by the application of applied stimulation if required. This requirement is analogous to iterative learning control and much progress, with supporting clinical trials data, has been reported on using this engineering design method to regulate the applied stimulation such that patient improvement in completing the task corresponds to increasing voluntary input and reduced stimulation. The applied stimulation in this application can induce muscle fatigue and this paper gives new results on enhancing the control laws to mitigate this unwanted effect.