Modeling Personalized Difficulty of Rehabilitation Exercises Using Causal Trees
This addresses the need for tailored rehabilitation in stroke survivors, though it is incremental as it builds on existing causal methods for personalization.
The paper tackles the problem of generic difficulty settings in rehabilitation exercises by developing a causal tree-based method to model personalized difficulty based on user performance, resulting in accurate and interpretable models for stroke survivors.
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.