Experiencia Afectiva de Usuario (UAX): Modelo desde sensores biométricos en aula de clase con plataforma gamificada de Interacción Gestual

La computación afectiva permite la sensibilización de la interacción humano computador, validando el estado emocional del usuario. Los niños en el ambiente escolar tienden a cambiar muy fácilmente su estado emocional por factores externos o propios de la sensibilidad de su edad. Se propone UAX como enfoque general para conocer el estado emocional del alumno durante su estimulación en el aula de clase gamificada con apoyo de una plataforma de interacción gestual TangoH. La validación es asíncrona, apoyada en datos fisiológicos obtenidos de sensores, desde los cuales se establece de forma objetiva la valoración emocional del alumno. El planteamiento se sostienes en dos fases: la primera fase contiene 3 momentos: Interacción, Pre-Procesamiento y resultados intermedios; la segunda fase refiere a una matriz general para la comparación de Emotion Data y un proceso para seleccionar el mejor modelo de ML aplicando las curvas de ROC. Se presenta resultados obtenidos a partir de datos simulados de interacción, siguiendo los parámetros y formatos de datos estándares devueltos por sensores de este tipo

Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability

Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public's trust in AVs. Many factors can influence people's trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people's dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his or her knowledge, perception, experience, behavioral assessment, and feelings about AVs. These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously

Emotional Design: An Overview

Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.http://deepblue.lib.umich.edu/bitstream/2027.42/163319/1/Emotional_Design_Manuscript_Final.pdfSEL

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

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Habilitador para colaboração no design para a customização em massa em micro e pequenas empresas

Orientador: Prof. Dr. Adriano HeemannTese (doutorado) - Universidade Federal do Paraná, Setor de Artes, Comunicação e Design, Programa de Pós-Graduação em Design. Defesa : Curitiba, 18/12/2019Inclui referências: p. 117-133Resumo: O processo design de produtos voltados para a produção através da manufatura tradicional apresenta metodologias e ferramentas já consolidadas, assim como estudos que abordam a colaboração entre os atores desse processo. Porém, o processo tradicional de design apresenta limitações para conseguir responder as exigências necessárias para que seja possível implementar o design para customização em massa (CM) nas empresas atualmente. Uma das formas de facilitar o processo da customização em massa é utilizando-se do design colaborativo. Considerando a inexistência de conhecimento estruturado que habilite a colaboração no design para customização em massa, um quadro teórico elaborado nesta tese evidencia uma lacuna na utilização concomitante destes conceitos, apesar da literatura indicar a importância da colaboração dos participantes do processo de design para customização em massa. Assim a presente pesquisa responde como viabilizar a colaboração entre os participantes no design para a customização em massa. Essa problemática é abordada de forma a descrever como habilitar a colaboração entre os participantes no design para a customização em massa e isso é alcançado através do desenvolvimento de um habilitador para o design colaborativo para customização em massa. Para tanto, este documento descreve a identificação de fatores críticos de sucesso para a customização em massa e para a colaboração no design de produtos. Essa identificação é feita por meio de uma revisão de literatura e de estudos de caso. Os fatores identificados são, então, utilizados para o desenvolvimento do habilitador. Esse habilitador se deu em formato de e-book e foi desenvolvido com o intuito de disseminar conhecimento principalmente para micro e pequenas empresas do Brasil que ofertam produtos customizados em massa. Palavras Chave: Design colaborativo. Customização em massa. Desenvolvimento de produto. Habilitador.Abstract: The product design process focused on production through traditional manufacturing has already consolidated methodologies and tools, as well as studies that address collaboration between the actors in this process. However, the traditional design process has limitations in order to be able to meet the necessary requirements so that it is possible to implement the design for mass customization (MC) in companies today. One of the ways to facilitate the mass customization process is by using collaborative design. Considering the lack of structured knowledge that enables collaboration in design for mass customization, a theoretical framework elaborated in this thesis highlights a gap in the concomitant use of these concepts, despite the literature indicating the importance of the collaboration of the participants in the design process for mass customization. Thus, this research answers how to enable collaboration between participants in the design for mass customization. This issue is addressed to describe how to enable collaboration between participants in the design for mass customization and this is achieved through the development of an enabler for collaborative design for mass customization. To this end, this document describes the identification of critical success factors for mass customization and collaboration in product design. This identification is made through a literature review and case studies. The identified factors are then used for the development of the enabler. This enabler took place in e-book format and was developed with the aim of disseminating knowledge mainly to micro and small companies in Brazil that offer mass customized products. Key words: Collaborative design. Mass customization. Product development. Enabler

Metodología HCI con análisis de emociones para personas con Síndrome de Down. Aplicación para procesos de aprendizaje con interacción gestual.

La Interacción Humano-Computador (HCI) es el área emergente de Ciencias de la Computación más cercana al usuario, y define las características del diseño, evaluación e implementación de los sistemas interactivos centrados en las personas. Por su parte el Síndrome de Down (SD) es un trastorno genético que se estima aparece en uno de cada 660 nacidos vivos, y provoca cambios biológicos y psicológicos que limitan sus capacidades; distintos estudios afirman que esta población ha logrado mejores aprendizajes desde la estimulación viso-motriz. Desde este contexto se propone el diseño de una metodología HCI con análisis de emociones para personas con SD, con aplicación para procesos de aprendizaje con interacción gestual. En un proceso inicial se realiza un estudio de literatura, para conocer el estado del arte del ámbito de la propuesta, teniendo como enfoque la interacción natural desde ambientes gestuales, enfocados a ámbitos de educación inclusiva; se presenta un método personalizado para la revisión de literatura sostenido en el estilo de Kitchenham y cuatro preguntas de investigación; se encontró un espacio amplio sin explorar acerca de estudios experimentales para establecer la experiencia afectiva de usuario para niños y niñas con SD. Para cumplir el objetivo de estudio se propone una metodología de investigación científica sostenida en estándares de Usabilidad y UAX, desde procesos, modelos matemáticos y algoritmos verificables que sustenten el contexto teórico-práctico de la propuesta. Como resultado se obtiene: i) un estudio de Usabilidad de los recursos didácticos aplicados a individuos con SD; ii) modelo de estudio de Experiencia Afectiva de Usuario (UAX) desde múltiples fuentes de reconocimiento de emociones; iii) arquitectura general para adaptar la plataforma de interacción gestual utilizada y modelos de aprendizaje a un aula de clase inteligente