MAPPING EMOTIONS TO SHAPE PARAMETERS USING
ARTIFICIAL NEURAL NETWORK
Khusnun Widiyati1, Hideki Aoyama2
Department of System Design Engineering, Faculty of Science and Technology
3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522 Japan
1Email:
khusnun@ina.sd.keio.ac.jp
haoyama@sd.keio.ac.jp
2Email:
Abstract. Aesthetic and emotion are becoming an attractive factor for product design.
The product image in customer’s mind is a result from form impression process that
incorporates both aesthetic and emotion during product design. And therefore, there is a
need for sophisticated design based on terms expressing “Kanse”, which in this study
these terms are defined as “Kansei words”. Though researchers are trying to formalize
the relationship between shape information and the emotions, the knowledge about the
relation between shape parameters to the associated evoke emotion is still limited. This
paper attempts to propose an approach to map emotions to product shape parameters
using artificial neural network. To achieve this, recognition on product shape
parameters and collection of emotion/ Kansei words were becoming the first important
step. Several 2D models were then created based on the shape parameters. Assessment
of product images was carried out by presenting the model and emotion/Kansei words in
semantic differential survey to participants from design engineering students. Artificial
neural network was used to map the product image to shape parameters. An evaluation
of how users perceived the shapes was conducted to validate the artificial neural
network model. A case study on PET bottle also presented to give clear figure how the
mapping method works.
Keywords: Aesthetics, emotion/Kansei, artificial neural network, shape parameters