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Modelling affective aspects of human-artefact interaction based on Kansei engineering: application to the hairdryer domain

Mingcai Hu et al.

International Journal of the Digital Human2023https://doi.org/10.1504/ijdh.2023.133034article
AJG 1
Weight
0.26

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijdh.2023.133034

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@article{mingcai2023,
  title        = {{Modelling affective aspects of human-artefact interaction based on Kansei engineering: application to the hairdryer domain}},
  author       = {Mingcai Hu et al.},
  journal      = {International Journal of the Digital Human},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.1504/ijdh.2023.133034},
}

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.