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  11. Research On Wheelchair Form Design Based On Kansei Engineering And Gwo-bp Neural Network

Research on wheelchair form design based on Kansei engineering and GWO-BP neural network

Weilin Cai1, Zhengyu Wang1, Yi Wang1

  • 1School of Art Design and Media, East China University of Science and Technology, Shanghai, 200237, China.

Scientific Reports
|March 26, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel wheelchair design method using Kansei engineering, integrating the evaluation grid method, grey wolf optimization, and back propagation neural networks to align product aesthetics with user emotions for enhanced humanistic care.

Area of Science:

  • Industrial Design
  • Human-Computer Interaction
  • Computational Intelligence

Background:

  • Modern consumers value emotional and spiritual product benefits beyond mere functionality.
  • Wheelchair design traditionally focuses on function, often neglecting user emotional experience.
  • Kansei engineering offers a framework to bridge product design and user emotions.

Purpose of the Study:

  • To develop a wheelchair form design method incorporating Kansei engineering principles.
  • To explore the relationship between wheelchair design elements and user emotional responses.
  • To assist industrial designers in creating emotionally preferred wheelchair designs.

Main Methods:

  • Utilized the Evaluation Grid Method (EGM) to extract user-driven Kansei vocabulary.
Keywords:
Back propagation neural networkEvaluation grid methodGrey Wolf optimization algorithmKansei engineering

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  • Employed morphological analysis to build a product element sample library.
  • Applied semantic difference and factor analysis to identify critical Kansei demand factors.
  • Developed conceptual wheelchair designs using 3D modeling software.
  • Constructed a predictive model using a Grey Wolf Optimization (GWO)-optimized Back Propagation Neural Network (BPNN) to link design elements with Kansei images.

    • Main Results:

    • Identified three critical Kansei demand factors influencing wheelchair design.
    • The GWO-BPNN model demonstrated superior predictive ability compared to standard BPNN.
    • The proposed method effectively links wheelchair design features to user emotional preferences.

    Conclusions:

    • The integrated Kansei engineering approach provides a robust method for emotionally resonant wheelchair design.
    • The GWO-BPNN model offers enhanced predictive performance for design-emotion mapping.
    • This research offers a new perspective for algorithm-driven wheelchair design focused on user emotional needs.
    Wheelchair design