A computer based system to design expressive avatars

Avatars are used in different contexts and situations: e-commerce, e-therapy, virtual worlds, videogames,collaborative online design... In this context, a good design of an avatar may improve the user experience. The ability of controlling the way an avatar convey messages and emotions is capital. In this work, a procedure to design avatar faces capable of conveying to the observer the most suitable sensations according to a given context is developed. The proposed system is based on a combination of genetic algorithms and artificial neural networks whose training is based on perceptual human responses to a set of faces.Diego-Mas, JA.; Alcaide Marzal, J. (2015). A computer based system to design expressive avatars. Computers in Human Behavior. 44:1-11. doi:10.1016/j.chb.2014.11.027S1114

Designing the appearance of environmentally sustainable products

The study presented in this paper uses a mathematical model to measure the degree in which a product will be perceived as environmentally friendly from its physical attributes. A model based on genetic algorithms and neural networks was developed to predict the judgement of the users about environmental friendliness of different tables. Opinions of real users about a large set of tables were used to train the model. The results of the study suggest that, using this procedure in advanced stages of product design process, designers can determine the set of product's physical attributes that best convey the idea of environmentally sustainable to the customer. The analysis of the obtained model allows establishing how different product's attributes influence users' perception. From these results, the utilization of users' affective response models to design the appearance of environmentally sustainable products is discussed.Diego-Mas, JA.; Poveda Bautista, R.; Alcaide Marzal, J. (2016). Designing the appearance of environmentally sustainable products. Journal of Cleaner Production. 135(1):784-793. doi:10.1016/j.jclepro.2016.06.173S784793135

Understanding Customers' Affective Needs with Linguistic Summarization

Abstract: To increase the chance of launching a successful product into market, it is essential to satisfy customers’ affective needs during the product design stage. However, understanding customers’ affective needs is very difficult task and product designers might misunderstand the customers’ affective needs. In this study, linguistic summarization with fuzzy set is used to present customers’ affective needs with natural language statements which could be easily understood by human beings. The relations between customers’ affective needs and product design elements are represented by type-I and type-II fuzzy quantified sentences. To illustrate the applicability of the linguistic summarization with fuzzy set in translating customers’ affective needs to natural language statements, a case study is conducted on mobile phone design. The results indicate that the linguistic summarization with fuzzy set can be a useful tool to assist designers to create products satisfying affective needs of customer

Single users' affective responses models for product form design

This paper presents a neural network based approach to modeling consumers' affective responses for product form design. A theoretical framework for a single user's perception is developed. On the basis of this theoretical framework, a mathematical model which enables single users' responses to different products to be predicted was developed. The results obtained show that the mathematical models developed achieved highly accurate predictions. For the purpose of obtaining a global model various individual mathematical models were created, which were based on the opinions of users representing different groups of opinion. The results suggest that, under some conditions, the combined use of various models of individual users can perform as well as a single model generated on the basis of mean market responses.Diego-Mas, JA.; Alcaide Marzal, J. (2016). Single users' affective responses models for product form design. International Journal of Industrial Ergonomics. 53:102-114. doi:10.1016/j.ergon.2015.11.005S1021145

Understanding and modeling of aesthetic response to shape and color in car body design

This study explored the phenomenon that a consumer's preference on color of car body may vary depending on shape of the car body. First, the study attempted to establish a theoretical framework that can account for this phenomenon. This framework is based on the (modern-) Darwinism approach to the so-called evolutionary psychology and aesthetics. It assumes that human's aesthetic sense works like an agent that seeks for environmental patterns that potentially afford to benefit the underlying needs of the agent, and this seeking process is evolutionary fitting. Second, by adopting the framework, a pattern called “fundamental aesthetic dimensions” was developed for identifying and modeling consumer’s aesthetic response to car body shape and color. Next, this study developed an effective tool that is capable in capturing and accommodating consumer’s color preference on a given car body shape. This tool was implemented by incorporating classic color theories and advanced digital technologies; it was named “Color-Shape Synthesizer”. Finally, an experiment was conducted to verify some of the theoretical developments. This study concluded (1) the fundamental aesthetics dimensions can be used for describing aesthetics in terms of shape and color; (2) the Color-Shape Synthesizer tool can be well applied in practicing car body designs; and (3) mapping between semantic representations of aesthetic response to the fundamental aesthetics dimensions can likely be a multiple-network structure

Affective design using machine learning : a survey and its prospect of conjoining big data

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today’s competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design

An image evaluation approach for parameter-based product form and color design

[[abstract]]The parameter-based technique provides an efficient and valid means of constructing 3-D geometric models in many CAD software systems. However, its use is generally restricted to the design of mechanical components with regular configurations, and it is not ideally suited to product form and color design. This paper proposes a rapid conceptual design approach, which creates color-rendered forms and combines parameter-based features with fuzzy neural network theorems and gray theory to predict their image evaluation. Two evaluation models (Evaluation Model I and Evaluation Model II) are developed and applied in a case study of an electronic door lock design. Model I uses a fuzzy neural network to predict the overall image, while Model II uses a gray clustering operation for the color image evaluation and two fuzzy neural networks for the form image evaluation and the overall image evaluation. The results show that the image prediction capability of Model II is superior to that of Model I (RMSE: 0.062 versus 0.105). Furthermore, the overall image evaluation is dominated by the door lock's color rather than by its form (RMSE: 0.071 versus 0.162). The dominance of color in determining the image evaluation may be due to the specified image words, form evolution restrictions, or the membership grade ranges of the test color samples and the test form samples, etc. Having established the superiority of Model II, it is applied to develop a consultative design interface integrated with a professional CAD system in order to demonstrate the effectiveness of the proposed product design and image evaluation approach. The design system presented in this study enables a designer to predict the likely image tendencies of a designed product without the need to create and test a prototype model. Hence, he or she can make any design parameter modifications necessary to ensure that the finished product meets its specified image goals

A FRAMEWORK FOR CONCEPT VALIDATION IN DESIGN USING DIGITAL PROTOTYPING

Ph.DDOCTOR OF PHILOSOPH