Modeling Personalized Difficulty of Rehabilitation Exercises Using Causal Trees
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
Rehabilitation robots are often used in game-like interactions for
rehabilitation to increase a person's motivation to complete rehabilitation
exercises. By adjusting exercise difficulty for a specific user throughout the
exercise interaction, robots can maximize both the user's rehabilitation
outcomes and the their motivation throughout the exercise. Previous approaches
have assumed exercises have generic difficulty values that apply to all users
equally, however, we identified that stroke survivors have varied and unique
perceptions of exercise difficulty. For example, some stroke survivors found
reaching vertically more difficult than reaching farther but lower while others
found reaching farther more challenging than reaching vertically. In this
paper, we formulate a causal tree-based method to calculate exercise difficulty
based on the user's performance. We find that this approach accurately models
exercise difficulty and provides a readily interpretable model of why that
exercise is difficult for both users and caretakers.
