FuzEmotion as a backward kansei engineering tool

Kansei engineering, also known as kansei ergonomics or emotional engineering, aims at analysing and incorporating customer s feeling and demands into product function and product design. Founded in the late 1970 s, kansei is now considered as a key consumer-oriented technology for new product development. This paper described a system called FuzEmotion for the purpose of assessing the kansei aspects of a product by considering design attributes of a product. Fuzzy logic is used to represent kansei words and process fuzzy input. The system has been successfully implemented to ascertain gender inclination of a mobile phone. Principal parameters of a mobile phone are considered, i.e., length, width, thickness, and mass. The system can inform gender inclination of a mobile phone with accuracy up to 76%. This is based on a set of 92 mobile phone samples from the five major mobile phone manufacturers.     

感性工学中的参数化设计方法关键技术研究现状与展望

 感性工学(KE)是将消费者感性体验融入设计中的常用方法。过去十余年间,国内 外学者对 KE 展开了研究并取得了一定成果。通过对国内外 KE 中的参数化设计方法关键技术 研究成果进行梳理,首先总结了 KE 中常用的感性意象与产品特征参数化方法;随后重点介绍 了 3 种 KE 模型构建问题上的常用方法及其特点,并将现有研究进行了对比;最后讨论了 KE 现存问题并对未来可能的研究趋势进行了展望。     

A proposal of the event-related potential method to effectively identify kansei words for assessing product design features in kansei engineering research

The effective acquisition of kansei words for assessing product design features plays a decisive role in knasei engineering research, whereas previous studies rarely give a full understanding of how to effectively grasp the related kansei words. The existing findings revealed that traditional methods based on questionnaire surveys might have trouble and limitations in kansei words acquisition. The process requires the active involvement of users, which could be time-consuming, expensive, knowledge and labor intensive. Hence, there still remains a need for an efficient method that can effectively identify kansei words for assessing product design features in kansei engineering study. The N400, an event-related potential (ERP), can be triggered by semantic violations in visual stimulus materials. The present study investigates whether N400 can be used as an electrophysiological measurement to effectively identify kansei words for assessing product design features in kansei engineering research. This study takes the SUVs’ pictures as the prime stimulus and two relatedness-match levels of kansei words from a high level to a low level as the target stimulus, including the unrelated kansei words and related kansei words. It is shown that the related kansei words elicit a shorter N400 amplitude than the unrelated kansei words. Moreover, the N400 exhibits the different scalp distributions between related and unrelated kansei words. These findings indicate that the N400 could be used to compensate limitations of conventional questionnaire surveys in kansei words acquisition for assessing product design features.Relevance to industryWith the assistance of N400 neural measurement in kansei words acquisition, kansei engineering will advance further as a modernized technology in kansei product design. The method proposed in this study uses N400 to effectively identify kansei words, which can be applied to kansei engineering research and improve the product development process.     

Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

利用颜色和熵提取感兴趣区域的感性图像检索

感性图像检索是一种新型的检索技术，这种检索具有较高的复杂性。一幅图像中能够使人们产生感性认识的可能只有部分区域，准确地找到感兴趣区域能有效地降低复杂度。作为图像基本特征的颜色对人的感觉有重要的影响，颜色的差异和对比使人产生了不同的情感。同时，图像的熵也反应了图像中包含信息量的大小，图像的熵也是引起人们产生感性认识的一个度量。提出了利用图像的颜色和熵提取感兴趣区域进行感性图像检索的方法，通过BP神经网络将感兴趣区域的颜色特征和熵映射到情感特征空间，具有较好的检索效果。     

产品感性评价系统的模糊D-S推理建模方法与应用

建立了用户对产品的感性评价与模糊D-S证据理论的关联模型;利用产生式规则来表达用户感性评价知识;以产品的部件外形来构成推理的证据部分;以感性词汇集及相应的语意差分值来定义规则的目标集.通过模糊D-S证据理论的规则合成算法,最终获得了用户对整体产品评价及可靠度.该方法能有效地解决感性认知中的"未知性"及评价日标的单一性问题,并在以汽车为例的概念设计中得到较好应用.     

Automatic digital mood boards to connect users and designers with kansei engineering

One challenge for designers is how to express emotions to clients when helping them analyze ideas for the development of products. Mood boards, which comprise a set of images and words, are one of the most common tools for synthesizing a client's perception and instructing the designer about visual communication. The creation of these boards is time-consuming and becomes static before the end of the design process. This article investigates the possibility of building a kansei engineering system that is based on rough set probability statistics and is capable of linking kansei words obtained from clients with images that can be continuously collected online. The result is a proposal for a new kansei engineering procedure that contains five cycles and captures users' opinions in all phases of the design process. A subset of real data is used in the application of this procedure to a consumer product, which demonstrates the feasibility of this kind of application. The article presents a complete theoretical model of this system and its procedures and algorithms, which enables the creation of automatic mood boards and connects designers to users' needs.     

A dominance-based rough set approach to Kansei Engineering in product development

Keen competitions in the global market have led product development to a more knowledge-intensive activity than ever, which requires not only tremendous expert knowledge but also effective analysis of design information. Kansei Engineering as a customer-oriented methodology for product development, often has to analyse imprecise design information inherent with nonlinearity and uncertainty. This paper proposes a systematic approach to Kansei Engineering based on the dominance-based rough set theory. Two novel concepts known as category score and partition quality have been developed and incorporated into the proposed approach. The new approach proposed is able to identify and analyse two types of inconsistencies caused by indiscernibility relations and dominance principles respectively. The result of an illustrative case study shows that the proposed approach can effectively extract Kansei knowledge from imprecise design information, and it can be easily integrated into an expert system for customer-oriented product development.     

Is the background knowledge of ergonomists important if ergonomics is to succeed within a simultaneous engineering (SE) environment?

Successful implementation of simultaneous engineering (SE) depends upon a number of factors. Meaningful communication among the team members is one factor. While most members on the SE multidisciplinary team have a common foundation, engineering, it is generally the ergonomist who lacks such background and becomes the weakest member of the team. Instead of being a problem solver, he/she is frequently treated as an irrelevant element in the scheme of things. This paper shows that simple ergonomic recommendations for product design could result in economically undesirable consequences for the manufacturer. Therefore, the ergonomist on the SE team must understand and consider the economic and technical impact of his/her recommendations on consumer product manufacturing and, in consultation with the engineering designer and process engineer, determine the best way to implement them. This obviously requires a thorough understanding of manufacturing and the adoption of an interdisciplinary role on the part of ergonomists. Furthermore, discussions involving the significance and importance of ergonomics should be carried out in forums primary to manufacturing (Society of Manufacturing Engineers, Institution of Production Engineers, Journals of Design and Manufacturing, etc.) and process engineers so that the main objective — consideration of ergonomics in product manufacture — may be achieved cost effectively.

Relevance to industry

Successful implementation of simultaneous engineering should lead to usable and reliable products that can be made easily, quickly, efficiently, and economically. Consideration of ergonomic design guidelines is essential in order to manufacture such products. Therefore, for market success, manufacturers must ensure that ergonomists participating in multidisciplinary SE design teams have background in both design and manufacturing technology.     

基于感性工学的文具产品形态设计研究

随着社会发展,消费者对于产品的精神和情感需求越来越高。因此,如何有效的获取消费者的心理情感需求,并进行有效转化至产品设计之中,成为设计中的新课题。感性工学正是在这种情形下产生,其旨在探求消费者情感与产品特性的对应关系,服务于消费者。本文将以文具设计为例,简述感性工学在文具设计中的应用。     

灰关联分析在产品感性设计中的应用研究

本文以青年男性眼镜为例进行灰关联分析在产品感性设计中的应用研究实验,通过灰关联分析模型分析设计元素与消费者感性评价数据之间的关系,提取对于时尚男性眼镜设计最具影响力的三个设计元素。通过实验表明,灰关联分析模型可以帮助设计师在进行感性设计时更加快速和准确地把握产品的设计要点,提高产品设计的时效性。     

A product configuration analysis method for emotional design using a personal construct theory

Identifying emotion-related product attributes (perceived by consumers) is no easy task in the realm of emotional design. Conventionally, this process relies heavily on the researchers who conduct the Kansei experiments selecting product attributes such as color, form, and texture for Kansei studies. However, in so doing, other product attributes that also play a vital role in product-emotion associations might be neglected by the researchers. More importantly, the identification of product attributes should be based on consumer's point of view (and feelings). Accordingly, a personal construct theory based product configuration analysis method is proposed in this work. The method develops the customer's mind map for each Kansei tag in order to capture replications of candidate products. A means-value chain is used to generate targets which are later compared with candidate products by consumers. The comparison results could suggest product attributes that are relevant to the desired Kansei. The proposed approach is presented and illustrated using a case study of Graffiti designs on notebooks. Results obtained are discussed. It appears that the proposed method is promising in identifying product attributes with desired Kansei impacts.     

基于犹豫模糊语言术语集和粒子群优化算法的产品造型设计感性评价方法

产品造型感性评价反映了用户的意象感知,具有模糊性与不确定性,用户常难以准确描述其感性偏好而表现出犹豫.针对该问题,引入犹豫模糊语言术语集(HFLTSs)描述用户感性评价,基于其数学算子构建犹豫模糊语言共识模型以测度用户认知一致性程度,借助粒子群优化算法(PSO)实现非共识条件下用户评价矩阵的优化与共识达成,通过逼近理想...     

Incorporating affective customer needs for luxuriousness into product design attributes

In a highly competitive market, customers' product affection is a critical factor to product success. However, understanding customers' affective needs is difficult to grasp; product design practitioners often misunderstand what customers really want. In this study we report our experience in developing and using an affective design framework that identified critical affective features customers have on products and are systematically incorporated into product design attributes. To identify key affective features such as luxuriousness, we utilized the Kansei engineering methodology. This approach consists of three steps: (1) selecting related affective features and product design attributes through a comprehensive literature survey, expert panel opinion, and focus group interviews; (2) conducting evaluation experiments; and (3) developing Kansei models using multivariate statistical analysis and analyzing critical product design attributes. To demonstrate applicability of the proposed affective design framework, 30 customers and 30 product design practitioners participated in an evaluation experiment for car crash pads, and 44 customers and 20 designers participated in an evaluation experiment for two interior room products (wallpapers and flooring materials). The evaluation experiments were conducted via systematically developed questionnaires consisting of a 7‐point semantic differential scale and a 100‐point magnitude estimation scale. The results of the experiments were analyzed using principal component regression and quantification theory type I method. Using the analyzed survey data, the relationship between luxuriousness and related affective features and product design attributes were identified. This relationship indicated that there was a significant difference in the perception of luxuriousness between customers and designers. Consequently, it is expected that the results of this study could provide a foundation for developing affective products. © 2009 Wiley Periodicals, Inc.     

Applying Kansei engineering to design logistics services – A case of home delivery service

Home delivery service (HDS) has grown fast in recent years because more and more customers engage in Internet and television shopping. Moreover, due to the rising competition in the HDS sector, service providers need to put more concentration on the continual improvement and differentiation of service to maintain the competitive advantage. After experiencing the current services, customers are always expecting the new ones. Thus, the idea of new service design never ends. Designers always have to insight into the real feelings of customers so that they could design the new service, which is able to meet customers' expectation. Kansei engineering is a means to transform real voices of customers into the product and service design. This method is able to quantify the relationship between feelings represented by simple words and design elements. In this study, the Partial Least Square (PLS) is used to analyze the relationships between the real feelings of customers and characteristics of HDS. Finally, this study combines those characteristics, which are strongly related to important feelings, to obtain the original idea of new design of HDS.Relevance to industryAs stated in this abstract, our study offers an exemplification on applying Kansei engineering to design service in service industries. Specifically, along with the application of Kansei engineering, the research findings offer a technique for service design of service industries.     

基于产品感性定制模型的顾客协同设计系统

为使产品定制模型更加适合缺少相关领域专业知识的大众消费者,建立了基于感性工学的产品感性定制模型.引入配件感性性能指数、产品感性性能矩阵对产品感性性能进行量化.使用层次分析法实现了求解与顾客对产品感性性能需求对应的产品工程配置的方法.并应用产品感性定制模型,构建了基于Web和虚拟现实技术的顾客协同设计系统.     

面向大众消费者的产品感性定制模型及其应用

为了使产品定制模型更加适合缺少相关领域专业知识的大众消费者,建立了基于感性工学的产品感性定制模型.将顾客对产品的感性意象作为定制需求,对其进行分类并量化,利用层次分析法评价待选的产品工程配置实例,获得最符合顾客感性意象需求的产品工程配置.最后用自行车定制系统实例说明了该模型的应用.     

感性微分法在产品设计中的探讨

感性工学是将感性与工学相结合的一种技术,其在产品设计中的应用,主要通过分析人的感性来建立产品构想模型,并据此来设计产品。感性微分法是感性工学中的一种定性研究方法,这种方法可以通过感性概念的逐步细分从而和设计元素建立起一一对应关系,本文试图应用感性微分法在产品设计中的作用进行探讨,并以行李箱的拉杆设计为例就感性微分法的应用进行探讨。     

多意象驱动下的产品造型个性化定制系统研究

构建了一种基于多意象驱动下的产品个性化定制系统,增加用户对个性化定制的体验度,从而实现智能的产品个性化定制设计。通过对当下三种产品个性化定制模式的比较研究和分析,提出了个性化定制系统的构架模型和系统流程。然后通过用户参与模式、感性意象挖掘、关联方法建立多意象驱动机制,并结合感性工学的相关研究方法、BP神经网络以及多目标粒子群算法构建了面向产品意象造型的智能设计模型,实现了多意象驱动下的产品造型个性化定制。最后以人形卡通文具的个性化定制进行了实例验证。结果表明多意象驱动的产品造型个性化定制系统更加丰富了消费者对产品造型意象风格的体验,满足了消费者日益迫切的个性化设计需求,符合智能时代对产品设计开发的需要。