Abstract
In product design, it is important to understand user’s kansei values and reflect them in the product. We then need to identify the relationship between kansei evaluations and product features. A large amount of text data including users’ impressions and kansei evaluations of products are stored on the Web as review data and various indices have been developed to evaluate products sensitively based on these. However, no method has been established to regress these evaluation indices on the design proposal using optimization. In this study, we propose the rudimentary design method for obtaining design proposal (product features) that satisfy users’ kansei requirements by using multi-objective optimization with regression models and validate effectiveness of the proposed method through experiment on real products.
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Fukuhara, S., Lee, S., Arakawa, M. (2023). Development of Multi-objective Optimal Design Method Using Review Data with Kansei Items as the Objective Function. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1835. Springer, Cham. https://doi.org/10.1007/978-3-031-36001-5_77
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DOI: https://doi.org/10.1007/978-3-031-36001-5_77
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