The work aims to investigate the mechanism through which bionic furniture design features influence consumers' emotional responses, and construct a "design features and emotional resonance" analysis model based on fuzzy semantic computation to reveal how the three dimensions of distinctiveness, integration, and interactivity affect emotional experience. A questionnaire survey of 477 consumers was conducted to collect data on their perceptions and emotional feedback toward the form, structure, function, and interaction of bionic furniture. Responses were measured with a five-point Likert scale and then converted into triangular fuzzy numbers for fuzzy semantic computation. Exploratory factor analysis was applied to verify the structural validity of the scale, and an ordinal logistic regression model was used to analyze the direction and significance of the effects of the three design dimensions on ten categories of emotional responses. Distinctiveness, integration, and interactivity all had significant positive effects on consumers' emotional responses (p<0.05). Distinctiveness exerted the strongest influence on satisfaction and pleasure (β=0.503), integration significantly enhanced comfort and trust (β=0.317), and interactivity improved emotional engagement and user experience (β=0.183). The model fit index (χ2) and Nagelkerke R2 (0.133-0.271) indicated strong explanatory power. The emotional resonance mechanism of bionic furniture design follows a hierarchical process of "perception, experience, and behavior". Distinctiveness stimulates visual interest, integration maintains functional and aesthetic harmony, and interactivity deepens emotional engagement and satisfaction. The study confirms the applicability of fuzzy semantic computation in emotional design analysis, though it remains limited by regional sampling and subjective evaluation. Future research will integrate multimodal emotional data and physiological feedback to establish more predictive computational models for bionic design.
Key words
bionic furniture design /
fuzzy semantic computation /
ordinal logistic regression /
emotional resonance /
design features
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References
[1] 周涛, 孙培贤. 符号诠释项下产品仿生形态设计方法[J]. 包装工程, 2022, 43(6): 119-124.
ZHOU T, SUN P X.Bionic Shape Design Method of Products under Symbol Interpretation[J]. Packaging Engineering, 2022, 43(6): 119-124.
[2] 刘琳琳, 李烜, 李明, 等. 融合外形仿生与可拓创新方法的产品创新设计[J]. 机械设计, 2024, 41(8): 190-195.
LIU L L, LI X, LI M, et al.Product Innovation Design Integrating Bionic Shape and Extension Innovation Method[J]. Journal of Machine Design, 2024, 41(8): 190-195.
[3] 许永生, 王伟, 张润芝, 等. 基于生态美学的产品形态仿生设计与评价研究[J]. 设计艺术研究, 2023, 13(4): 48-53.
XU Y S, WANG W, ZHANG R Z, et al.Research on Bionic Design and Evaluation of Product Form Based on Ecological Aesthetics[J]. Design Research, 2023, 13(4): 48-53.
[4] 侯士江, 王建暖, 刘甲财, 等. 叉车造型的视觉动力分析及其意象仿生设计研究[J]. 包装工程, 2021, 42(22): 108-114.
HOU S J, WANG J N, LIU J C, et al.Visual Dynamic Analysis of Forklift Modeling and Its Image Bionic Design[J]. Packaging Engineering, 2021, 42(22): 108-114.
[5] YU S, LIU M B, CHEN L P, et al.Emotional Design and Evaluation of Children's Furniture Based on AHP-TOPSIS[J]. BioResources, 2024, 19(4): 7418-7433.
[6] SETCHI R, ASIKHIA O K.Exploring User Experience with Image Schemas, Sentiments, and Semantics[J]. IEEE Transactions on Affective Computing, 2019, 10(2): 182-195.
[7] CAI W L, WANG Z Y, WANG Y, et al.Research on Wheelchair Form Design Based on Kansei Engineering and GWO-BP Neural Network[J]. Scientific Reports, 2025, 15: 10258.
[8] ZHU L, YAN Y Q, LV J F.A Bibliometric Analysis of Current Knowledge Structure and Research Progress Related to Sustainable Furniture Design Systems[J]. Sustainability, 2023, 15(11): 8622.
[9] 任洁. 面向模糊信息和模糊语义的不确定性度量及其应用[D]. 北京: 北京邮电大学, 2024: 2-5.
REN J.Uncertainty Measure for Fuzzy Information and Fuzzy Semantics and Its Application[D]. Beijing: Beijing University of Posts and Telecommunications, 2024: 2-5.
[10] ZADEH L A.Toward a Theory of Fuzzy Systems[M]. New York: WORLD SCIENTIFIC, 1996: 83-104.
[11] SHOAIP N, EL-SAPPAGH S, ABUHMED T, et al.A Dynamic Fuzzy Rule-Based Inference System Using Fuzzy Inference with Semantic Reasoning[J]. Scientific Reports, 2024, 14: 4275.
[12] LI F Y, SHANG C J, LI Y, et al.Approximate Reasoning with Fuzzy Rule Interpolation: Background and Recent Advances[J]. Artificial Intelligence Review, 2021, 54(6): 4543-4590.
[13] KAUFMANN A, GUPTA M M.Introduction to Fuzzy Arithmetic[M]. New York, N.Y.: Van Nostrand Reinhold Co., 1991.
[14] CHIEN C J, TSAI H H.Using Fuzzy Numbers to Evaluate Perceived Service Quality[J]. Fuzzy Sets and Systems, 2000, 116(2): 289-300.
[15] HSU T H, LIN L Z.Fuzzy Set Theory for Building Analysis Matrixes of Travel Risk[J]. Sun yet-sent Management Review, 2005, 13(2): 479-509.
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