Applying Latent Dirichlet Allocation and Support Vector Regression to the Aesthetic Design of Medical Nursing Beds

Abstract

Abstract With the development of social productivity and the improvement in material living standards, emotional value has become the core driver of the enhancement of product market competitiveness. A medical nursing bed, one of the most typical types of medical devices, is designed with little attention to the emotional experience of the users. Therefore, this paper proposes an innovative perceptual design approach under the Kansei engineering (KE) framework for resource-limited and information-poor companies. It guides the aesthetic design of medical nursing beds by constructing a mapping relationship between users’ perceptual needs and the design characteristics of medical nursing beds to maximize users’ emotions. First, latent Dirichlet allocation (LDA) is used to extract usable Kansei semantics from big data, compensating for the subjectivity of traditional KE data input. Then, the design characteristics obtained after deconstructing a medical nursing bed are simplified with rough set theory (RST). Finally, a mapping model between users’ perceptual needs and the core design characteristics of nursing beds is established through support vector regression (SVR), and the optimal design solution is obtained by weighting calculation. The optimal combination of design characteristics for medical nursing beds is finally obtained. The results suggest that the design method proposed in this paper can help designers accurately grasp users’ emotional perceptions in terms of aesthetic design and scientifically guide and complete the design of new medical nursing beds, verifying the feasibility and scientificity of the proposed method in terms of aesthetic design.