Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals

Abstract

Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individual's performance. We show that this technique explains the variance in user performance for a reaching task better than previous approaches to estimating task difficulty.

Accepted to the 2023 IROS Workshop on Assistive Robots for Citizens