Modelling affective aspects of human-artefact interaction based on Kansei engineering: application to the hairdryer domain
by Mingcai Hu; Fu Guo; Zenggen Ren; Hao Shao; Vincent G. Duffy
International Journal of the Digital Human (IJDH), Vol. 2, No. 3, 2023

Abstract: Recently in ergonomics and human factors community, there have been calls for incorporating affective aspects, such as pleasure and aesthetics, for product development. This study presents a systematic approach to modelling Kansei, which is users' subjective feeling and impression, by combining variable precision rough sets (VPRS) and association rule mining. The design element reducts corresponding to each Kansei attribute are firstly extracted using β-partition quality-based attribute reduction algorithm. Subsequently, the Apriori algorithm was adopted to induce middle-order association rules. The empirical results involving appearance design of hairdryer domain demonstrate the usefulness of the adoption of VPRS. The induced rules can serve the purpose of working memory and inference engine of a virtual Kansei engineering system, which provides a potential research line for modelling affective aspects of human-artefact interaction in the community of digital human modelling.

Online publication date: Fri, 25-Aug-2023

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