title: Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: methodology and preliminary report.
abstract: Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.
authors.affiliation: Department of Computer Science, University of Central Oklahoma, Edmond, OK.
authors.forename: Jicheng
authors.initials: J
authors.lastname: Fu
authors.forename: Maria
authors.initials: M
authors.lastname: Jones
authors.forename: Yih-Kuen
authors.initials: YK
authors.lastname: Jan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008328:Man-Machine Systems; D008962:Models, Theoretical; D066230:Patient-Specific Modeling; D011187:Posture; D003668:Pressure Ulcer / Q000209:etiology / Q000517:prevention & control*; D012039:Regional Blood Flow; D012867:Skin / Q000098:blood supply* / Q000648:ultrastructure; D013119:Spinal Cord Injuries / Q000150:complications / Q000534:rehabilitation*; D018608:Ultrasonography, Doppler; D014910:Wheelchairs / Q000009:adverse effects
keywords: C4.5 decision tree; artificial neural network; machine learning; pressure ulcer; random forest; skin blood flow; skin perfusion; spinal cord injury; support vector machine; wheelchair tilt and recline