Decision support for stroke rehabilitation therapy via describable attribute-based decision trees

Vinay Venkataraman, Pavan Turaga, Nicole Lehrer, Michael Baran, Thanassis Rikakis, Steven L. Wolf

Research output: Contribution to journalArticle

Abstract

This paper proposes a computational framework for movement quality assessment using a decision tree model that can potentially assist a physical therapist in a telerehabilitation 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.

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Decision Trees
Decision trees
Patient rehabilitation
Rehabilitation
Physical Therapists
Hand Strength
Biomechanical Phenomena
Upper Extremity
Health Care Costs
Survivors
Therapeutics
Stroke
Cones
Kinematics
Stroke Rehabilitation
Costs
Datasets

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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title = "Decision support for stroke rehabilitation therapy via describable attribute-based decision trees",
abstract = "This paper proposes a computational framework for movement quality assessment using a decision tree model that can potentially assist a physical therapist in a telerehabilitation 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.",
author = "Vinay Venkataraman and Pavan Turaga and Nicole Lehrer and Michael Baran and Thanassis Rikakis and Wolf, {Steven L.}",
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AU - Venkataraman, Vinay

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AU - Lehrer, Nicole

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AU - Rikakis, Thanassis

AU - Wolf, Steven L.

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