About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

User kansei modeling and eco-design

The use of day-to-day life artifacts is a key phase in the lifecycle of products. Indeed it has a great impact on our environment. User centered methods are not yet taken into account in eco-design approaches. These methods are being developed in two ways, by building new user models encompassing complex dimensions such as Kansei and experience, including values and moods, and by integrating the user himself in the design process. This paper deals with setting-up a new theoretical framework associating user-centered design advanced approaches such as experience design, Kansei studies, or participative design and eco-design methods. The final goal is to support product design by providing some guidelines according to environmental issues linked to the users and their abilities

Cognitive Designers Activity Study, Formalization, Modelling, and Computation

This study aims to explore how designers mentally categorise design information during the early sketching performed in the generative phase. An action research approach is particularly appropriate for identifying the various sorts of design information and the cognitive operations involved in this phase. Thus, we conducted a protocol study with eight product designers based on a descriptive model derived from cognitive psychological memory theories. Subsequent protocol analysis yielded a cognitive model depicting the mental categorisation of design information processing performed by designers. This cognitive model included a structure for design information (high, middle, and low levels) and linked cognitive operations (association and transformation). Finally, this paper concludes by discussing directions for future research on the development of new computational tools for designers

Case-based reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables

Key form features are relative to the style of a product and the expression style features depict the product description and are a measurement of attribute knowledge. The uncertainty definition leads to an improved and effective product style retrieval when combined with fuzzy sets. Firstly, a style knowledge and features database are constructed using fuzzy case based reasoning technology (FCBR). A similarity measurement method based on case-based reasoning and fuzzy model of the fuzzy proximity method may be defined by the Fuzzy Nearest-Neighbor (FNN) algorithm obtaining the style knowledge extraction. Secondly, the Linguistic Variables (LV) are used to assess the product characteristics to establish the product style evaluation database for simplifying the style presentation and decreasing the computational complexity. Thirdly, the model of product style feature set, extracted by FAHP and the final style related form features set, are acquired using LV. This research involves a case study for extracting the key form features of the style of high heel shoes. The proposed algorithms are generated by calculating the weights of each component of high heel shoes using FAHP with LV. The case study and results established that the proposed method is feasible and effective for extracting the style of the product

Exploring the KEER knowledge landscape over the past decade

The aim of this paper was to systematically explore the knowledge landscape of papers presented at KEER conferences over the last decade. We collected all papers published in conference proceedings between 2010 and 2020. We (i) used a text mining pipeline to extract, clean, and normalize keywords from the Title and Abstract fields, and (ii) created a co-occurrence network reflecting the relationships between keywords. The network was then characterized at different levels of granularity (static analysis vs. time slice analysis and whole network vs. node-level analysis). The exploratory analysis showed a stable expansion of the network over time. The cluster structure revealed several groups of keywords that did not change over time and reflected both domain-specific and method-specific topics of research in Kansei engineering

Generation of product design using GAN based on customer's kansei evaluation

In recent years, deep learning has attracted much attention and various techniques have been proposed. GAN (Generative adversarial networks) is one such method. GAN uses images as the training set and learns to generate new images that are indistinguishable from the training set. In this study, A GAN-based design method that generates new products from the images of the customer's favorite products is proposed. The product images that customers evaluated as preferable are used as the training set of GAN. If the GAN fulfills its capabilities properly, the images generated from a customer's favorite product are more likely to be preferred by the customer. In the case study, the proposed method was applied to chair design. The generated chair images were first evaluated in terms of image quality, and then evaluated by subjects

Meta-KANSEI modeling with Valence-Arousal fMRI dataset of brain

Background: Traditional KANSEI methodology is an important tool in the field of psychology to comprehend the concepts and meanings; it mainly focusses on semantic differential methods. Valence-Arousal is regarded as a reflection of the KANSEI adjectives, which is the core concept in the theory of effective dimensions for brain recognition. From previous studies, it has been found that brain fMRI datasets can contain significant information related to Valence and Arousal. Methods: In this current work, a Valence-Arousal based meta-KANSEI modeling method is proposed to improve the traditional KANSEI presentation. Functional Magnetic Resonance Imaging (fMRI) was used to acquire the response dataset of Valence-Arousal of the brain in the amygdala and orbital frontal cortex respectively. In order to validate the feasibility of the proposed modeling method, the dataset was processed under dimension reduction by using Kernel Density Estimation (KDE) based segmentation and Mean Shift (MS) clustering. Furthermore, Affective Norm English Words (ANEW) by IAPS (International Affective Picture System) were used for comparison and analysis. The data sets from fMRI and ANEW under four KANSEI adjectives of angry, happy, sad and pleasant were processed by the Fuzzy C-Means (FCM) algorithm. Finally, a defined distance based on similarity computing was adopted for these two data sets. Results: The results illustrate that the proposed model is feasible and has better stability per the normal distribution plotting of the distance. The effectiveness of the experimental methods proposed in the current work was higher than in the literature. Conclusions: mean shift can be used to cluster and central points based meta-KANSEI model combining with the advantages of a variety of existing intelligent processing methods are expected to shift the KANSEI Engineering (KE) research into the medical imaging field

Computational Methods for Shape Manipulation in generation : a literature review

In this paper we will present a state of the art of the descriptive and generative models for shape. We will present several different approaches for the manipulation of shape in computational systems: numerical models, graph models, descriptive models. This investigation will lead to a discussion regarding the use of these models for supporting the generation of shapes in the early phases of the design process.ANR GENIUS (TECHLOG-07-010

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research