Research on rehabilitation following a stroke has consistently identified treatment intensity and goal oriented strategies as critical for successful therapeutic outcomes. The current prognosis for upper limb recovery following stroke is poor, with the literature reporting that complete recovery occurs in less than 15 % of patients with initial paralysis. Stroke is also an age-related disease, placing an increasing burden on long-term health and related resources unless improvements are made in achieving independence. Consequently there is a pressing need to improve the effectiveness of treatments.

To further maximize rehabilitation after stroke, novel therapeutic and cost-effective rehabilitation methods, or interventions, are required, which may combine different methodologies. For example, one possibility is to combine the application of assistive stimulation with robot-aided therapy and virtual reality. The premise is that this approach, supported by mobile technology, could be a major step towards enabling rehabilitation outside the hospital, where two of the major objectives are increased intensity of therapy and reduced cost.

To be accepted for use by health professionals any new method requires development of technology and clinical trials to establish feasibility. This monograph is based on a research programme that aims to combine the use of electrical stimulation, virtual reality and iterative learning control for upper-limb stroke rehabilitation. Iterative learning control was especially developed for systems, such as a gantry robot executing pick and place of objects, which repeat the same finite time task over and over again. Once each task is complete, the system resets and information generated during its completion is available for use in updating the control action to be applied during the next execution of the task.