感性自動計測システムの提案と灰色理論による数量化

概要

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This paper proposes a kansei automatic measurement system (KAMS) and kansei information quantization method based on grey theory. A sensory evaluation using adjective words (kansei words) needs many subjects and a lot of time. A grasp of minimum subject number (MSN) is necessary in order to efficiently acquire the trustful data. Proposed method transforms kansei information into the quasi-liner space. KAMS which is an online application with slider acquires kansei data from experiments. Kansei information named affect interval is acquired from kansei data by grey relational analysis. Affect interval is a grey number that quantified kansei information. Maximum value and minimum value of grey relational grade use an endpoint of the affect interval. MSN is shown mathematically. In addition, an experiment which used the proposal method for the kansei data of landscape pictures is carried out. We have confirmed that the MSN is 11 or more subjects.
日本感性工学会の論文