Modeling Personalized Difficulty of Rehabilitation Exercises Using Causal Trees

TL;DR

This paper introduces a causal tree-based method to personalize rehabilitation exercise difficulty, accounting for individual differences in stroke survivors' perceptions, thereby improving motivation and outcomes.

Contribution

It presents a novel causal tree approach to model personalized exercise difficulty, addressing limitations of generic difficulty assumptions in rehabilitation robots.

Findings

Accurately models individual exercise difficulty

Provides interpretable insights for users and caretakers

Enhances motivation and rehabilitation outcomes

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.