Kansei Evaluation Research Articles

Relationship Between Kansei Evaluation and Physical Characteristics of Automotive Switches

Relationship Between Kansei Evaluation and Physical Characteristics of Automotive Switches

Kansei evaluation on shape design of PET bottle

Kansei evaluation on shape design of PET bottle

Construction of a Classical Music Database for Research Purposes in the Field of Kansei Evaluation

Construction of a Classical Music Database for Research Purposes in the Field of Kansei Evaluation

Analysing Implicit Emotion and Unity in Propaganda Videos Posted in Social Network

In today’s world of technology involving the sophisticated use of the Internet, the political scene of a country evolves alongside the prolific rise of social media where it becomes a battlefield for political parties to win the hearts and minds of the people through videos, e.g. political discourse and radical videos – involving many issues such as politics, religious, social and economics, which have been posted through social media. These propaganda videos would be powerful vehicles for prejudice and public opinions that could affect national unity specifically among the people. Kansei, has been established as being well-suited to be applied for the purpose of understanding human emotion and psychology. Henceforth, this study aims to comprehend the sensitivity of the people towards various propaganda videos and also to determine whether a relationship exists between Kansei and the videos. In this sense, a Kansei evaluation approach is employed on 10 videos, using 30 Kansei Words, which are adopted from the PANAS-X emotional descriptors. From the results obtained, it is evident that the people do have political awareness, sensitivity and are able to respond to the videos that represent various political issues that could affect people’s unity. Besides, Kansei is able to verify the results, thus proving the existence of the concept of Kansei in propaganda videos. Eventually, the results are significant in providing an overture to people’s response towards political issues, especially through the medium of videos.

A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling

Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.

The Contrast Impact of Two-Piece Garments and KANSEI Evaluation

The Contrast Impact of Two-Piece Garments and KANSEI Evaluation

Recommender system based on personal kansei evaluation tendency

Recommender system based on personal kansei evaluation tendency

Physiological Measurement and Kansei Evaluation of Steering Feeling Affected by Steering Wheel Torque

Physiological Measurement and Kansei Evaluation of Steering Feeling Affected by Steering Wheel Torque

KANSEI Evaluation of Skin Color in Face Image – Faces of Japanese Young Females-

KANSEI Evaluation of Skin Color in Face Image – Faces of Japanese Young Females-

Hierarchy of Evaluation by “CDM (Curve Drawing Method)” for Time-series Kansei Evaluation

Hierarchy of Evaluation by “CDM (Curve Drawing Method)” for Time-series Kansei Evaluation

Emergency Service Provision Using a Novel Hybrid SOM-Spiral STC Model for Group Decision Support under Dynamic Uncertainty

In emergency scenarios service vehicles must identify potential route(s) and use the best available route. However, route identification requires intelligent decision-support systems which generally use non-traditional approaches with tools characterised by flexible non-hierarchical structures. Conventional models using group decision-support systems have been applied; however, when used in smart urban environments, emergency response services have limitations in their ability to identify unobstructed paths (routes) in dynamic operating environments. In this paper we introduce a novel path planning method for autonomous vehicle control in emergency situations. The proposed model uses self-organizing maps in an integrated Spiral STC algorithm termed the: Hybrid SOM-Spiral STC model which uses hedge algebras and Kansei evaluation in group decision-support. The proposed model has been designed to quantify qualitative factors using sensor derived data processed with human sensibilities and preferences in emergency decision support. The experimental results show that the proposed model achieves significant improvements in group decision-support under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in the provision of emergency services.

Personalized Product Evaluation Based on GRA-TOPSIS and Kansei Engineering

With the improvement of human living standards, users’ requirements have changed from function to emotion. Helping users pick out the most suitable product based on their subjective requirements is of great importance for enterprises. This paper proposes a Kansei engineering-based grey relational analysis and techniques for order preference by similarity to ideal solution (KE-GAR-TOPSIS) method to make a subjective user personalized ranking of alternative products. The KE-GRA-TOPSIS method integrates five methods, including Kansei Engineering (KE), analytic hierarchy process (AHP), entropy, game theory, and grey relational analysis-TOPSIS (GRA-TOPSIS). First, an evaluation system is established by KE and AHP. Second, we define a matrix variate—Kansei decision matrix (KDM)—to describe the satisfaction of user requirements. Third, the AHP is used to obtain subjective weight. Next, the entropy method is employed to obtain objective weights by taking the KDM as input. Then the two types of weights are optimized using game theory to obtain the comprehensive weights. Finally, the GRA-TOPSIS method takes the comprehensive weights and the KMD as inputs to rank alternatives. A comparison of the KE-GRA-TOPSIS, KE-TOPSIS, KE-GRA, GRA-TOPSIS, and TOPSIS is conducted to illustrate the unique merits of the KE-GRA-TOPSIS method in Kansei evaluation. Finally, taking the electric drill as an example, we describe the process of the proposed method in detail, which achieves a symmetry between the objectivity of products and subjectivity of users.

Hybrid quality function deployment method for innovative new product design based on the theory of inventive problem solving and Kansei evaluation

This study proposes a new hybrid method that adopts the theory of inventive problem solving and the Kansei evaluation in quality function deployment processes to facilitate innovative new product design and evaluation in the early design phase. The hybrid model and method procedures consist of four stages. First, user satisfaction needs are identified based on a questionnaire of linear numeric rating scale, factor analysis, and an analytic hierarchy process, and the completeness and reliability of this identification are guaranteed by the use of Cronbach’s coefficient alpha statistic. Second, crucial design zones are identified by a correlation matrix, and analyzing the interrelationship matrix at the quality function deployment to establish critical innovation points. Third, the main tools of the theory of inventive problem solving are applied to address these critical innovation points. As such, several innovative alternatives are designed by combining the suitable inventive principles, design rulers, and crucial design zones. Finally, a general and rough set for the Kansei evaluation of the best design alternative is presented. Innovative car seat design is conducted to scientifically and efficiently verify this proposed method.

Emotional augmented reality-based mobile learning design elements: a kansei engineering approach

<span>In recent times, various studies have shown that Augmented Reality (AR) will be the next wave of online learning. This is because of the advent of powerful smartphones that has changed user experiences, thereby able to increase the capability of AR. There has been much concentration in previous studies on cognition towards the use of AR in education, in which little consideration has been given to emotions which is also an important aspect in learning. Based on this, the present research aims to identify salient connections between emotions and design elements of AR-based mobile learning material through the application of the Kansei Engineering (KE) approach. In order to achieve this study objective, the use of a human heart in relation to the mobile AR application of the KE approach was adopted in this research as a case study, in which seven specimens of the mobile AR application were evaluated including 55 emotions of Kansei Words (KW). Additionally, the kansei evaluation experiment of this study was carried out by 28 students from one of the public universities, after which the data were analysed using Factor and Principal Component Analysis. The results of this study show the important pillars of emotions or Kansei semantic space of emotions for AR-based mobile learning materials. Based on Factor Analysis, it revealed four main pillars; <em>professional-motivated</em>, <em>confused</em>, <em>wandering-thrilled</em>, <em>challenging</em> and one additional pillar; <em>trustable</em>. Besides that, this research also described design elements of AR-based mobile learning material that might evoke specific emotions based on the identified pillars. Finally, the findings of this research are hoped to be applicable as a guide in design during preparation of AR-based mobile learning materials with affective elements in the future.</span>

Effect of Directional and Diffused Lights on the KANSEI Evaluation of a Glossy Object and the Scene

Effect of Directional and Diffused Lights on the KANSEI Evaluation of a Glossy Object and the Scene

Retrieval of Semantic-Based Inspirational Sources for Emotional Design.

In the conceptual design stage, inspirational sources play an important role in designers' creative thinking. This paper proposes a retrieval method for semantic-based inspirational sources, which helps designers obtain inspirational images in the conceptual design stage of emotional design. The core principle involves solving the designer's own deficiencies in associations and limited knowledge, by bridging the “semantic gap” faced by designers when they use Kansei words for inspirational sources. This method can be divided into two aspects: (1) based on the semantic richness of Kansei words, the first part describes how a lexical ontology for Kansei words called KanseiNet is constructed and proposes a spreading activation mechanism based on KanseiNet to complete the semantic expansion of Kansei words; (2) the second part describes how, using existing semantic techniques, relevant design website resources are crawled and analyzed, images' context descriptions and Kansei evaluations are extracted, and Kansei evaluation index of inspirational images is established. The KanseiNet for Chinese is first constructed, and the Sources of Inspiration Retrieval System for Emotional Design (SIRSED) is developed. An experiment comparing the existing image retrieval systems with SIRSED proved the latter to be a more comprehensive and accurate way for designers to access inspirational sources.

The Possibility of the CDM (Curve Drawing Method) to Time-series Kansei Evaluation

The Possibility of the CDM (Curve Drawing Method) to Time-series Kansei Evaluation

Reuse of Kansei Evaluation Results for the Aesthetic Design of Different Types of Products

Reuse of Kansei Evaluation Results for the Aesthetic Design of Different Types of Products

Interactive technology embedded in fashion emotional design

PurposeThe purpose of this paper is to bridge the gap between human emotions and wearable technologies for interactive fashion innovation. To consider the reasons why smart clothing should satisfy the internet of things (IoT) technical functions and human emotional expression simultaneously, to investigate the manner in which artistic design perspectives and engineering methods combined effectively, to explore the R&D elements of future smart clothing based on the IoT technology.Design/methodology/approachThis study combines artistic design perspectives with information-sensing engineering methods as well as kansei evaluation method. Micro-sensors and light-emitting diodes (LEDs) embedded in couples clothing prototype. The first experiment step in the design and production of prototype clothing, and do the initial emotional evaluation. The second experiment is the comparative evaluation of the prototype and other typical smart clothing.FindingsThe interactive clothing prototype was proven to correlate well with human emotional expressive patterns. The evaluation I indicated the prototype can stimulate the emotional response of the participants to achieve a higher score in the activate sensor state. Evaluation II revealed that in the process of interactive clothing design, the technical functionality should synchronize with the requirements of human emotional expression.Originality/valueThis study builds the research and development theoretical model of interactive clothing that can be integrated into daily smart clothing life design, and analyze the methods and means of blending IoT smart information-sensing technology with emotional design. By means of this experimental demonstration of human-centered interactive clothing design, the authors provide smart clothing 3.0 evolutionary roadmap and propose a new concept of internet of clothes (IoC) for further research reference.

CBCRS: An open case-based color recommendation system

In this paper, a case-based color recommendation system (CBCRS) is proposed for online color ranges (CRs) recommendation. This system can help designers and consumers to obtain the most appropriate CR of consumer-products (e.g., garments, cars, architecture, furniture …) based on the color image perceptual data of each specific user. The proposed system is an open system, permitting to dynamically integrate new CRs by progressively learning from users’ and designers’ perceptual data. For this purpose, a Color Image Space (CIS) is initially established by using Basic Color Sensory Attributes (BCSAs) to obtain the color image perceptual data of both designers and consumers. Emotional Color Image Words (CIWs) representing CRs are measured in the proposed CIS through a knowledge-based Kansei evaluation process performed by designers using fuzzy aggregation operators and fuzzy similarity measurement tools. Using this method, new CIWs and related CRs from open resources (such as new color trends) can be integrated into the system. In a new recommendation, user's color image perceptual data measured in the proposed CIS regarding different BCSAs will be compared with those of CIWs previously defined in the system in order to recommend new CRs. CBCRS is an adaptive system, i.e. satisfied CRs will be further retained in a Successful Cases Database (SCD) so as to adapt recommended CRs to new consumers, who have similar user profiles. The general working process of the proposed system is based on case-based learning. Through repeated interactions with the proposed system by performing the cycle of Recommendation – Display - Evaluation – SCD adjustment, users (consumer or designer) will obtain satisfied CRs. Meanwhile, the quality of the SCD can be improved by integrating new recommendation cases. The proposed recommendation system is capable of dynamically generating new CIWs, CRs and new cases based on open resources.