用因果树建模中风患者康复动作的个性化难度
Modeling Personalized Difficulty of Rehabilitation Exercises Using Causal Trees
- 基于用户表现构建因果树，分析动作难度成因
- 不同患者对相同动作的难度感知差异显著
- 模型可解释性强，适合临床与康复指导
康复机器人常通过游戏化交互提升患者完成康复训练的动机。通过根据用户表现动态调整动作难度，可同时优化康复效果与参与意愿。以往方法假设所有用户对动作难度感知一致，但我们发现中风患者对动作难度存在个体差异：例如部分患者认为垂直伸手比远距离低处伸手更难，而另一些则反之。本文提出一种基于因果树的方法，依据用户表现计算个性化动作难度。结果表明该方法能准确建模难度，并提供可解释的机制说明，帮助患者和护理人员理解为何某动作困难。
原文摘要 · Abstract (English)
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.
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