Kansei Evaluation Research Articles

Frequency and Kansei Evaluation of Tones that Evoke Weight in the Impact Sound of an Object Landing with VR

Frequency and Kansei Evaluation of Tones that Evoke Weight in the Impact Sound of an Object Landing with VR

Form generative approach for front face design of electric vehicle under female aesthetic preferences

Vehicles are the most representative product of both transportation and industry. Fueled by the growing popularity of energy-saving and environmentally friendly ideas and policies, new energy technologies and their supporting industries are experiencing constant development and refinement. As a result, electric vehicles (EVs) are steadily displacing traditional fuel-powered vehicles as the dominant force in the vehicle market. The experience economy is driving a significant shift in the direction of EV development, which is transitioning from improving functionality to optimizing the emotional connection consumers have with their vehicles. This means the form factor of the vehicle itself is becoming a key element in attracting consumers and influencing purchasing decisions. Over half of China's EV consumer market is comprised of females. However, many companies continue to develop products solely from a male perspective, missing out on a vast market opportunity. Therefore, incorporating design elements that cater to female aesthetic preferences into EVs can undoubtedly play a crucial role in unlocking this significant segment of the market. Based on this, the paper proposes a form generative design approach that specifically caters to the preferences of female consumers within the theoretical framework of Kansei engineering.The research process begins by obtaining Kansei imagery through the collection and analysis of big data. Subsequently, eye-tracking experiments are conducted to define the key design elements of the automobiles. Concurrently, appropriate aesthetic measurement indicator (AMI) system is established. Next, AMI values are computed for the sample set. This data is then leveraged to create a mapping relationship with the Kansei imagery, enabling the objective reflection of subjective aesthetic evaluations. Finally, an integrated Kansei evaluation equation serves as the fitness function within a generative approach that utilizes genetic algorithms and visual AI to produce form design solutions for electric vehicles. The evaluation confirms that the proposed design approach successfully generates a substantial number of high-quality creative solutions that align with female aesthetic preferences, while maintaining low cost and high efficiency.

Construction of Product Appearance Kansei Evaluation Model Based on Online Reviews and FAHP: A Case Study of Household Portable Air Conditioners

Meeting the personalized needs of users is the key to achieving the sustainable success of a product. It depends not only on the product’s functionality but also on satisfying users’ emotional needs for the product’s appearance. Therefore, researchers have been conducting research focusing on Kansei engineering theory to determine users’ emotional needs effectively. The initial process involves accurately extracting and filtering emotional data and Kansei words from consumers. Thus, we propose an evaluation model to efficiently obtain, screen, and sort these Kansei words based on Kansei engineering, using household portable air conditioners as research subjects. By integrating techniques for online user comment mining methods, users’ Kansei terms related to the product’s appearance can be gathered efficiently. These terms are then combined with image samples and filtered to determine a final set of 16 Kansei word pairs. Subsequently, the fuzzy analytic hierarchy process (FAHP) is utilized to prioritize these terms, and the fuzzy comprehensive evaluation (FCE) method is used to validate the results and determine the applicability of the evaluation model. The results showed that Kansei words could be quickly and objectively acquired using existing text mining techniques on online reviews. Moreover, the weights of different Kansei terms of the product’s appearance in the consumer’s perception are accurately produced through the FAHP. This evaluation model marks a significant advancement in accurately obtaining users’ emotional data in Kansei engineering. It offers valuable guidance for designing products that meet users’ personalized needs, enhancing design efficiency and reducing resource wastage at the early stages of designing, and improving the sustainability development of Kansei engineering.

Integrated triangular fuzzy KE-GRA-TOPSIS method for dynamic ranking of products of customers’ fuzzy Kansei preferences

As society evolves, companies produce more homogeneous products, shifting customers’ needs from functionality to emotions. Therefore, how quickly customers select products that meet their Kansei preferences has become a key concern. However, customer Kansei preferences vary from person to person and are ambiguous and uncertain, posing a challenge. To address this problem, this paper proposes a TF-KE-GRA-TOPSIS method that integrates triangular fuzzy Kansei engineering (TF-KE) with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, a Kansei evaluation system is constructed based on KE and fuzzy theory. A dynamic triangular fuzzy Kansei preference similarity decision matrix (TF-KPSDM) is defined to quantify customer satisfaction with fuzzy Kansei preferences. Secondly, dynamic objective weights are derived using Criteria Importance Though Intercrieria Correlation (CRITIC) and entropy, optimized through game theory to achieve superior combined weights. Thirdly, the GRA-TOPSIS method utilizes the TF-KPSDM and combined weights to rank products. Finally, taking the case of Kansei preference selection for electric bicycles, results indicate that the proposed method robustly avoids rank reversal and achieves greater accuracy than comparative models. This study can help companies dynamically recommend products to customers based on their Kansei preferences, increasing customer satisfaction and sales.

Impact of Regular Pattern Design on Kansei Evaluation in Tomato Packaging

Impact of Regular Pattern Design on Kansei Evaluation in Tomato Packaging

A consumer-oriented kansei evaluation model through online product reviews

Generating kansei profiles for products represent fundamental aspects of kansei engineering (KE). Conventionally, the semantic differential (SD) method has been extensively employed to construct product kansei profiles, aiming to delve into consumers’ perceptions of products. However, this approach is associated with significant time consumption and inefficiency. In light of this, we introduce an innovative kansei evaluation approach that incorporates consumers’ kansei preferences, thereby enhancing the efficiency of the evaluation process. This approach comprises three integral modules: Firstly, the generation of product kansei profiles and the construction of a kansei database for decision alternatives are achieved through the analysis of online reviews. Subsequently, the kansei data is adjusted based on consumers’ kansei preferences. Finally, the rank correlation analysis (RCA) is conducted to establish the prioritization of decision alternatives. Notably, this method facilitates the ranking of products in accordance with consumers’ kansei preferences, thereby assisting consumers in navigating through an array of functionally similar products to identify their preferred choices. A comprehensive case study illustrates the implementation procedure and validates the practicality of our proposed method.

The Design of Automotive Interior for Chinese Young Consumers Based on Kansei Engineering and Eye-Tracking Technology

The reasonable CMF (Color, Material and Finishing) design for automotive interiors could bring positive psychophysical and affective responses of customers, providing an important guideline for automobile enterprises making differentiated products. However, current studies mainly focus on an aspect of CMF design or a single style of the automotive interior, and examined the design mainly through human visual perception. There lack systematic studies on the design and evaluation of automobile interior CMF, and more scientific evaluation of the design through human visual and touching perception was required. Therefore, this study systematically designed the automobile interior CMF based on Kansei engineering and eye-tracking technology. The study consists of five steps: (1) Product positioning: the Chinese young consumers, the new energy vehicles, and bridge and seat are the target users, the automotive model and the key interior components. (2) Kansei physiological measurement: nine groups of Kansei words and thirty-three interior samples were selected, and the interior samples were scored by the Kansei words. (3) Kansei data analysis: three design types were determined, i.e., “hard and stately”, “concise and technological” and “comfortable and safe”. Meanwhile, the CMF design elements of the automotive interiors under the three styles were obtained through mathematical methods. (4) Design practice: four CMF samples under each design style (12 samples) were developed. (5) Kansei evaluation: the design themes were conducted using eye-tracking technology, and the optimal sample that mostly satisfy the user’s Kansei requirements under each style was obtained. The proposed design process of automotive interior CMF may have great implications in the design of automotive interiors.

A product form design method integrating Kansei engineering and diffusion model

The experience economy has shifted consumer demands from the functional to the emotional, and the emotional demands of the user have become a key design consideration. At the same time, product form design often relies solely on the knowledge and experience of designers, resulting in uneven design results and difficult quality assurance. To this end, this paper proposes a product form design method that applies an image generation algorithm oriented toward satisfying the emotional demands of users. Firstly, product images from the network are collected and processed to build a dataset of product images, which is used to train the Diffusion Model (DM) to generate product images that differ from the dataset. Secondly, the Kansei factors are obtained by clustering Kansei words from online reviews using Factor Analysis (FA) and then calculating the weights of the Kansei factors by the Analytic Hierarchy Process (AHP). Thirdly, a questionnaire is distributed to obtain user scores on the Kansei factors of the product images, and the Kansei evaluation value is calculated by weighting, then a prediction model is constructed using Support Vector Regression (SVR) to score and filter the generated images. Finally, the designer selects the highest-scoring images for detailing and tests the effectiveness of the design through user satisfaction. Using the ear thermometer as an example, we have created a product form that meets the emotional demands of users and verifies the scientific validity and effectiveness of the proposed method.

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

The market scale of electric shavers in China has reached ¥ 26.3 billion in 2021. Consumers currently place an increasing emphasis on the Kansei image conveyed by products rather than just concerning with functional satisfaction. To meet consumers’ expectations, the emotional message conveyed by product design is essential under multisensory channels. This research first collected 230 electric shavers samples and 135 pairs of consumers’ Kansei words, then reduced them into 34 representative samples using multidimensional scale and clustering analysis, with 4 groups of representative Kansei words selected via the expert group. Moreover, consumers’ Kansei images were evaluated via questionnaire using the semantic differential scales, with 416 valid samples acquired in total. Meanwhile, design elements of the samples (including item and category) were classified by ways of morphological analysis and audio software. At last, the prediction models of the electric shavers were established between the overall design elements and user’s Kansei evaluation under the multisensory channel of visual model and auditory audio taking advantage of Quantification Theory Type I , back propagation neural network, and genetic algorithm‐based BPNN. The proposed models can provide defined design indexes and references in multisensory design, facilitating designers to design in a logical and scientific manner rather than designing as per experience.

RETRACTED: A hybrid model to develop aesthetic product design of customer satisfaction

With the improvement of manufacturing technology, the performance gap between different products has been gradually narrowed, and customers pay more and more attention to psychological feelings and aesthetic experiences in the process of purchasing and using products. Therefore, the main purpose of this research is to construct a hybrid model of aesthetic product design to develop customer satisfaction. Firstly, seven customer aesthetic needs are summarized by discussing the literature. The Kansei evaluation and customer satisfaction evaluation are investigated using Likert-scale, and the neural network is repeatedly trained and tested to obtain the weight of each aesthetic requirement. Secondly, the morphological analysis method is used to obtain the product morphological deconstruction table, and the entropy method is used to calculate the initial weights of the engineering characteristics (ECs). Finally, quality function development (QFD) is used as the platform to construct the relationship matrix between customer aesthetic needs and product ECs. Grey relationship analysis is used to calculate the final weight of ECs and obtain the priority of ECs. The research takes the side view of a car as a case. The results show that the best product form combination can provide a reference for designers and effectively improve the aesthetic experience and customer satisfaction of the product.

An improved Kansei engineering method based on the mining of online product reviews

The explosive growth of online review data on e-commerce websites demands major changes to product emotional design. Kansei engineering, the primary approach of product emotional design, faces multiple problems. This study aims to improve Kansei engineering method to adapt to the emotional design trends of the big data era. Targeting the data of online product reviews, the authors mined text data, calculated fine-grained emotions, and presented an innovative design flow for Kansei engineering. Firstly, a crawler program was compiled to mine online product reviews and product parameters from e-commerce websites, and the mined data were processed. Next, a Kansei word pair screening method was proposed based on term frequency – evaluation, potency, activity (TF-EPA), and used to obtain the Kansei word pairs depicting the imagery of product emotions. After that, a fine-grained calculation method for Kansei evaluation value of products was proposed based on term clustering and adverbs of degrees, and utilized to compute the evaluation value of each product sample under each Kansei word pair. Finally, the backpropagation (BP) neural network was extended into a mapping model between product parameters and Kansei evaluation values, and the prediction performance of the model was evaluated. The smart phone was taken as an example to verify the proposed method, The optimal training values of the 6 groups of Kansei word pairs were 0.00061, 0.00053, 0.00065, 0.00038, 0.00065, and 0.00093, respectively. The absolute error mean of 28 test sets in the experiment under 6 groups of Kansei word pairs were 0.0696, 0.1001, 0.0784, 0.0815, 0.1328, and 0.1358, respectively. Taking the third group as an example, the absolute error mean obtained by the multiple linear regression method was 0.1127 greater than 0.0784, which proved that the BP neural network method used in this paper was superior to the multiple linear regression method in accuracy.

A Study of a Latent Structure Analysis in Local Brands on Kansei Evaluation

A Study of a Latent Structure Analysis in Local Brands on Kansei Evaluation

Affective-Blue Design Methodology for Product Design Based on Integral Kansei Engineering

Sustainable product designs always draw much attention. However, sustainable or green products are usually costly. This contradiction can be solved via blue design. The concept of blue design originates from the blue economy which is a popular strategy for providing sustainable, healthy, but cheap socioeconomic activities. This study innovatively implements the ideas of sustainability and economy from the blue economy, and the affection (or Kansei in Japanese) from the Kansei engineering into a product design process to become a novel affective-blue design methodology of a product form. The proposed methodology mainly contains three aspects. The first aspect is the merge of a novel Kansei blue model with the traditional Kansei engineering to deal with the semantic space and form decomposition issues encountered in the product form designing process. The second aspect is the adoption of proper data mining schemes to optimally trim and obtain the kernel information from the Kansei evaluation data of products. The third aspect is the usage of appropriate machine learning schemes to establish a precise relationship between product images and design elements from the kernel information. A case study was conducted for the form design of a computer-numerical-control lathe to evaluate the effectiveness of our proposed methodology. The verification results, that all predictive errors are within 4.5% for test samples, show that our blue-affective design methodology is quite satisfying. Through applying this proposed methodology, designers may correctly evaluate and easily catch the essential blue and affective design factors for designing a good industrial product, such as a computer-numerical-control machine tool.

Kansei evaluation on shape design and color of PET bottle

In this study, we propose an evaluation method based on Kansei Engineering to clarify the main factors that affect the impression of the shape and label design of PET bottles. Focusing only on PET bottles of natural water, we classified them by clustering method using physical shape and label design features, evaluated them by Kansei engineering method, determined design parameters by Preference Set-Based Design (PSD) analysis, and clarified the main factors of impression. First, PET bottle samples were prepared, and cluster analysis was conducted to classify them into representative categories. These samples were subjected to online and offline sensory evaluation by various subjects, and the impressions of the PET bottles were clarified from the sensory evaluation. In order to eliminate the bias of the subjects, cluster analysis is conducted to classify them into representative categories with similar preferences for PET bottles. Finally, PSD analysis is conducted to identify the main factors that influence the impression of the bottle shape and label design.

Kansei evaluation for the impression of emotional expression given to avatars

This study estimates emotion from the speaker's voice signal and utterance character string. First, the acoustic features are extracted using openSMILE from the utterance voice acquired through the microphone, and emotions are estimated by classifying them into emotion classes by machine learning. Then, morphological analysis is performed from the spoken voice using a voice recognition engine, emotions are classified from the spoken character string, from where emotions are estimated. At the same time, facial expressions, head and neck movements are tracked by image analysis of video image of the speaker. After synthesizing and converting the estimated emotions, it will project onto the avatar to express the speaker's emotions with the facial expressions and movements of the avatar.

Creating food palatability: An introduction of Kansei evaluation

Creating food palatability: An introduction of Kansei evaluation

Factor Analysis for Differences in Impressions of Contents by PET Bottle Shape

The introduction of label-less bottles has been proposed as one of the measures to reduce plastic resin consumption for a recycling-oriented society. However, it is not easy to recognize the content of colorless transparent objects in a label-less PET bottle. This study uses mineral water and carbonated water for the content of PET bottle, and clarifies the characteristics of PET bottle shape design that can be easily distinguished between these two types of water. First, commercial PET bottles of both types of water were analyzed to define the features of shape design. Then, this paper explains the experiment of Kansei evaluation on PET bottle samples containing mineral water and carbonated water, and shows the result of analysis on the factors that determines the difference in impression. Finally, this paper will discuss the relationship between the impressions of the contents in PET bottles.

Relationship between sound perception and acoustics of seat‐belt warning sound

AbstractUnderstanding the comprehensive sound perceptions is a major challenge in translating the affective needs and responses of drivers and passengers into the human‐centered design of seat‐belt warning sound (SBWS). Due to the various sound impressions of SBWS, sound quality has multiple impacts on driving safety, pleasure, stress, and so forth. This paper aims to derive a specified psychological factor structure for the sound perceptions of SBWS and to rebuild the connections between the psychological and acoustic attributes. Kansei (emotional or affective) evaluation is employed to collect the affective responses to 20 sound stimuli from 10 experts and 134 other participants. The perceived sound quality is expressed by 11 psychological attributes: “cheerful,” “interesting,” “lovely,” “comfortable,” “nervous,” “cordial,” “advanced,” “crisp,” “nostalgic,” “aroused,” and “weak.” Russell's emotional state model is also employed to map the sounds into a Pleasure–Arousal Kansei space for further analysis. For acoustic attributes, we measure amplitude, frequency, pause ratio, repetition speed, and element number, which are easy to be tuned according to the design guidelines listed as follows: First, a decrease in sound frequency results in sleepiness perception (less perceived urgency); second, fewer element (peak) number per cycle results in displeasure perception (less satisfaction); third, lower repetition speed results in pleasure and sleepiness perceptions simultaneously. The multiple linear regression model further indicates the quantitative psychoacoustic relationships, which helps sound designers to identify the key acoustic attributes and predict the affective responses for the human‐centered design of SBWS.

Product innovation concept generation based on deep learning and Kansei engineering

Industrial designers often present their initial concepts as design sketches. Rapid creation of new product conceptual images that meet users’ affective preferences remains challenging in real design environments. However, few published works in affective design directly assist industrial designers in creating product conceptual images. Thus, we propose a product concept generation approach framework based on deep learning and Kansei engineering (PCGA-DLKE) to assist industrial designers. Our work focuses on dataset collection, pre-processing, affective preferences recognition, conceptual image generation model and product style transfer networks. To mark users’ affective preferences, we established an affective recognition model by Kansei engineering and deep convolutional neural networks. To address the product conceptual image generation problem, we proposed a product design GAN model (PD-GAN), generating product conceptual images with affective preferences. An improved fast neural style transfer network was successfully trained to meet users’ style preferences. This study aims to assist industrial designers in finding innovative concepts with affective preference. The Kansei evaluation shows that the innovation of the new product concept has been enhanced, indicating that the approach can better assist industrial designers in creating designs that meet users’ emotional needs. Hand drill design and bicycle helmet design are taken as a case study.

Kansei evaluation for group of users: A data-driven approach using dominance-based rough sets

Kansei refers to people’s subjective feeling and impression. Kansei evaluation devotes to assessing users’ preferences for product items according to multiple Kansei attributes, thus supporting the decision making of consumers and/or designers. The objective of this paper is to propose a data-driven approach for addressing user group oriented Kansei evaluation. The approach consists of three phases. The first phase identifies the representative Kansei attributes and product samples of the product domain to gather exemplary evaluation dataset from sampled representative users. In light of the specified Kansei need and relying on the dominance-based rough set approach, the second phase constructs the collective decision table so as to further infer the collective preferential information in terms of dominance-based decision rules and Kansei importance weights. The third phase presents a two-step sequential heuristic model for characterizing users’ affective preference behavior: (1) a multicriteria classifier using dominance-based decision rules for product sorting, partly simulating the satisficing heuristic; and (2) a simple choice strategy for product ranking, manifesting the CONF heuristic. A case study involving the toaster domain was conducted to verify the proposed approach. The theoretical and practical implications of the proposed approach are also discussed.