Research on T-shirt-style design based on Kansei image using back-propagation neural networks

Abstract

Abstract Users’ Kansei image preferences have become one of the most important factors influencing purchase decisions. However, defining Kansei image can be complex. To address this issue, researchers have widely applied back-propagation neural networks due to their capacity to handle extensive data, adaptively adjust weights and biases, conduct multi-class classification and regression predictions, and offer interpretability analysis, among other features. In this study, a clothing-style design model based on users’ Kansei image cognition is proposed, using collarless T-shirts as an example. Furthermore, the attributes of T-shirt patterns are quantified using parametric graphics principles, and a semantic scale system for emotions is established through user research. The quantified sample data and corresponding semantic scale scores are then used as inputs for training a back-propagation neural network algorithm. Consequently, a design model grounded in users’ Kansei image cognition is developed, resulting in five optimal clothing design forms across various Kansei image categories. Additionally, the styles are showcased through the Style 3D platform, and the design evaluation is presented using radar charts. The results demonstrate that the five female T-shirt designs generated by the model align with users’ style preferences based on Kansei image.