Decision support for stroke rehabilitation therapy via describable attribute-based decision trees.
Decision support for stroke rehabilitation therapy via describable attribute-based decision trees.
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通过可描述的基于属性的决策树为中风康复治疗提供决策支持。
DOI：
10.1109/embc.2014.6944292
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Wolf,StevenL
中科院分区：
文献类型：
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作者：
Venkataraman,Vinay;Turaga,Pavan;Lehrer,Nicole;Baran,Michael;Rikakis,Thanassis;Wolf,StevenL
本文提出了一个使用决策树模型的运动质量评估的计算框架，该模型可以潜在地在远程医疗环境中帮助理疗师。使用从8名中风幸存者收集的关键运动学属性的数据集，我们证明该框架可以可靠地用于伸手抓握锥体任务的运动质量评估，这是上肢中风康复治疗中常用的活动。拟议的框架能够提供与治疗师提供的评级高度相关的运动质量分数，治疗师使用康复专家创建的定制评级标准。我们的假设是，决策树模型可以很容易地被治疗师用作潜在的辅助工具，特别是在评估无监督康复(例如，在家中训练)期间收集的大规模数据集上的运动质量时，从而减少康复治疗的时间和成本。
This paper proposes a computational framework for movement quality assessment using a decision tree model that can potentially assist a physical therapist in a telereha-bilitation context. Using a dataset of key kinematic attributes collected from eight stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment of a reach-to-grasp cone task, an activity commonly used in upper extremity stroke rehabilitation therapy. The proposed framework is capable of providing movement quality scores that are highly correlated to the ratings provided by therapists, who used a custom rating rubric created by rehabilitation experts. Our hypothesis is that a decision tree model could be easily utilized by therapists as a potential assistive tool, especially in evaluating movement quality on a large-scale dataset collected during unsupervised rehabilitation (e.g., training at the home), thereby reducing the time and cost of rehabilitation treatment.