Journal of Rehabilitation Research & Development (JRRD)

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Volume 51 Number 5, 2014
   Pages 775 — 788

Abstract — Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: Methodology and preliminary report

Jicheng Fu, PhD;1* Maria Jones, PT, PhD;2 Yih-Kuen Jan, PT, PhD3

1Department of Computer Science, University of Central Oklahoma, Edmond, OK; 2Department of Rehabilitation Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK; 3Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL

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 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 showed 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.

Key words: artificial neural network, C4.5 decision tree, machine learning, pressure ulcer, random forest, skin blood flow, skin perfusion, spinal cord injury, support vector machine, wheelchair tilt and recline.


View HTML ¦ View PDF ¦ Contents Vol. 51, No.5

This article and any supplementary material should be cited as follows:
Fu J, Jones M, Jan Y. Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: Methodology and preliminary report. J Rehabil Res Dev. 2014;51(5): 775–88.
http://dx.doi.org/10.1682/JRRD.2013.09.0199
ResearcherID/ORCID: Jicheng Fu, PhD: J-3110-2013; Maria Jones, PT, PhD: G-7519-2014; Yih-Kuen Jan, PT, PhD: A-5374-2013
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