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.
authors.forename: Vinay
authors.initials: V
authors.lastname: Venkataraman
authors.forename: Pavan
authors.initials: P
authors.lastname: Turaga
authors.forename: Nicole
authors.initials: N
authors.lastname: Lehrer
authors.forename: Michael
authors.initials: M
authors.lastname: Baran
authors.forename: Thanassis
authors.initials: T
authors.lastname: Rikakis
authors.forename: Steven L
authors.initials: SL
authors.lastname: Wolf
mesh_terms: D000328:Adult; D000368:Aged; D001696:Biomechanical Phenomena; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D017741:Survivors; D014953:Wrist