A probabilistic approach to Kansei Profile generation in Kansei engineering

Abstract

As a methodology, Kansei Engineering (KE) has been developed to deal with consumers' subjective impressions and images of a product into the design elements of the product. One central step in KE is to generate Kansei profiles of the product. Traditional approaches to generating Kansei profiles, the average data model and voting based model, cannot model the underlying vagueness of Kansei data, in other words, they assume that any neighboring Kansei data have no semantic overlapping. This paper proposes a novel approach to generating Kansei profiles, which results with a probability distribution on Kansei data. The main advantage of our proposed approach is its ability to deal with partial semantic overlapping among Kansei data. The generated Kansei profiles can also be applied to consumer-oriented Kansei evaluation problems. This paper also discusses possible applications to fuzzy principal component analysis and fuzzy regression analysis.