Using Causal Trees to Estimate Personalized Task Difficulty in
  Post-Stroke Individuals
Nathaniel Dennler Stefanos Nikolaidis Maja Matarić
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.