# Modeling Personalized Difficulty of Rehabilitation Exercises Using Causal Trees

arXiv: https://arxiv.org/abs/2505.04583 · HTML: https://mlsanity.com/paper/2505.04583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 3 of 3)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Personalizing User Engagement Dynamics in a Non-Verbal Communication Game for Cerebral Palsy](https://mlsanity.com/paper/2107.07446/) (2021)
- [A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm](https://mlsanity.com/paper/2401.06974/) (2024)

## Abstract (arXiv)

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.