Exploring personalized fashion design process using an emotional data visualization method

Abstract

In recent years, rapid economic growth and a rising personal income have increased the demand for personalized services. To address this demand, the fashion industry and academia are increasingly analyzing and developing methods to provide personalized fashion design products. This study investigated an emotional data and data visualization-based design method for personalized fashion products. By visualizing emotions and involving consumers, we generated experimental designs to encourage interpersonal and emotional communication. In addition, we proposed methods for visualizing 28 levels of emotion in design elements, as well as a generative design process based on emotional and personal text messages. In fashion products, we used color and print to match the emotion and intensity of the emotion. As a result, 40 design experiment participants rated personalized fashion design tools, outcomes, and purchase intention positively. The highest score was received by the average value for expression of personality (4.43), purchase intention (4.38), and preference and recommendation (4.42). Consequently, this study could be applied to the use of personal data in generative fashion design, as well as the systemization of the data-driven design method for personalized and participatory fashion design.