Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals
This addresses the need for automated adaptation in stroke recovery programs, though it appears incremental as it builds on existing methods for estimating task difficulty.
The paper tackles the problem of quantifying personalized task difficulty for post-stroke individuals in adaptive training programs, showing that their method explains variance in user performance better than previous approaches.
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.