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

With the increasing emphasis on humanistic care in society, consumers are no longer only concerned about the functional needs of products but also about the spiritual, cultural, and emotional needs that products bring to people. This study proposes a wheelchair form design method based on the Kansei engineering approach, which integrates the evaluation grid method (EGM), grey wolf optimization (GWO) algorithm, and back propagation neural network (BPNN) technology. The aim is to explore the connection between wheelchair form design elements and user emotions and help industrial designers find designs with emotional preferences. In this study method, firstly, the collected wheelchair samples were evaluated using the EGM, extracting upper-level Kansei vocabulary driven by user attractiveness, middle-level original attractiveness items, and lower-level specific design elements and extracting nine sets of Kansei vocabulary mentioned frequently by users. Meanwhile, the morphological analysis method is used to construct a sample library of product morphological elements. Secondly, the semantic difference and factor analysis methods were used to analyze the ratings of 9 pairs of Kansei words, and the weights of Kansei factors were calculated to identify three critical Kansei demand factors. Thirdly, based on the analysis results of the orthogonal experiment, a conceptual plan for the wheelchair was constructed using computer-aided technology Rhinoceros 3D modelling software. Fourthly, the semantic difference method is used to collect users' ratings of critical Kansei words for wheelchair concept schemes, and the evaluation values of critical Kansei words are calculated by weighting. Fifth, a BPNN based on the GWO algorithm will establish a predictive model between wheelchair design elements and vital Kansei images. Finally, the predictive performance of BPNN and GWO-BPNN models will be compared to verify their superiority. The results indicate that the GWO-BPNN model has better predictive ability and performance. The method proposed by this research institute can more effectively help industrial designers create products that meet users' emotional needs, providing a new perspective for wheelchair algorithm design.