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Jicheng Fu, PhD; Maria Jones, PT, PhD; Yih-Kuen Jan, PT, PhD

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; Maria Jones, PT, PhD; Yih-Kuen Jan, PT, PhD. Aim

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Jicheng Fu, PhD; Maria Jones, PT, PhD; Yih-Kuen Jan, PT, PhD

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  1. 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; Maria Jones, PT, PhD; Yih-Kuen Jan, PT, PhD

  2. Aim • Demonstrate feasibility of using machine learning techniques to construct intelligent model to provide personalized guidance for individuals with spinal cord injury (SCI). • Relevance • Clinical evidence shows that SCI individuals’ requirements vary greatly. Hence, no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI.

  3. Method • Explored ways of modeling research participants. • Used machine learning techniques to construct the intelligent model. • Evaluated the intelligent model’s performance. • Further improved the intelligent model’s prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes.

  4. Results • Results demonstrated that our approaches were able to: • Effectively construct an intelligent model • Classify whether a given tilt and recline setting would be favorable for skin blood flow increase for an SCI individual, i.e., personalized guidance • Evaluate its performance. • Refine the participant model to significantly improve the intelligent model’s prediction accuracy.

  5. Conclusion • Our study demonstrated the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance on wheelchair tilt and recline usage to individuals with SCI. • The intelligent model achieved satisfactory accuracy by considering participants attributes that can be easily obtained without advanced clinical devices.

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