Machine Learning Enhanced User Interfaces for Designing Advanced Knitwear
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The relationship between visual appearance and structure and technical properties of a knitted fabric is subtle and complex. This is an area that has been traditionally problematic within the knitting sector, understanding between technologists and designers is hindered which limits the possibility of dialogues from which design innovation can emerge. Recently there has been interest from the Human-Computer Interaction (HCI) community to narrow the gap between product design and knitwear. The goal of this article is to show the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: “How can designers benefit from intelligent design software for the manufacturing of personalized advanced functionalities in textile products?”. In particular we explored how to design interactions and interfaces that use intelligent predictive algorithms through the analysis of a case study, in which several predictive algorithms were compared in the practice of textile designers.
KeywordsUser Interface Machine learning Knitwear
This work was supported by the National Natural Science Foundation of China (NSFC, Grant No. 51750110497). Many thanks for the generous support of Santoni Shanghai, Studio Eva x Carola, and the support from Yuanjin Liu and Yifan Zhang.
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