Optimizing the Level of Challenge in Stroke Rehabilitation using Iterative Learning Control: a Simulation

TL;DR

This paper introduces a simulation-based method using iterative learning control to optimize challenge levels in stroke rehabilitation, aiming to enhance patient engagement and motivation through adaptive difficulty adjustment.

Contribution

It presents a novel simulation framework that automatically adjusts task difficulty in stroke rehab using iterative learning control, improving upon threshold-based methods.

Findings

Achieves more challenging tasks compared to threshold-based updates.

Provides smoother difficulty progression in simulated stroke rehabilitation.

Demonstrates the effectiveness of iterative learning control in adaptive therapy simulation.

Abstract

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.