  The level of challenge in stroke rehabilitation has to be carefully chosen to
keep the patient engaged and motivated while not frustrating them. This paper
presents a simulation where this level of challenge is automatically optimized
using iterative learning control. An iterative learning controller provides a
simulated stroke patient with a target task that the patient then learns to
execute. Based on the error between the target task and the execution, the
controller adjusts the difficulty of the target task for the next trial. The
patient is simulated by a nonlinear autoregressive network with exogenous
inputs to mimic their sensorimotor system and a second-order model to
approximate their elbow joint dynamics. The results of the simulations show
that the rehabilitation approach proposed in this paper results in more
difficult tasks and a smoother difficulty progression as compared to a
rehabilitation approach where the difficulty of the target task is updated
according to a threshold.
