EMOTIONAL SYNCHRONIZATION-BASED HUMAN-ROBOT COMMUNICATION AND ITS EFFECTS

This paper presents a natural and comfortable communication system between human and robot based on synchronization to human emotional state using human facial expression recognition. The system consists of three parts: human emotion recognition, robotic emotion generation, and robotic emotion expression. The robot recognizes human emotion through human facial expressions, and robotic emotion is generated and synchronized with human emotion dynamically using a vector field of dynamics. The robot makes dynamically varying facial expressions to express its own emotions to the human. A communication experiment was conducted to examine the effectiveness of the proposed system. The authors found that subjects became much more comfortable after communicating with the robot with synchronized emotions. Subjects felt somewhat uncomfortable after communicating with the robot with non-synchronized emotions. During emotional synchronization, subjects communicated much more with the robot, and the communication time was double that during non-synchronization. Furthermore, in the case of emotional synchronization, subjects had good impressions of the robot, much better than the impressions in the case of non-synchronization. It was confirmed in this study that emotional synchronization in human-robot communication can be effective in making humans comfortable and makes the robot much more favorable and acceptable to humans.ArticleINTERNATIONAL JOURNAL OF HUMANOID ROBOTICS. 10(1):1350014 (2013)journal articl

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.SMDTex Project funded by the European Erasmus Mundus Progra

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Use of machine learning techniques in the Kansei engineering synthesis phase

Una de las principales metodologías para el diseño emocional de productos es la ingeniería Kansei. En esta técnica se pretende relacionar las propiedades del producto o servicio con las sensaciones percibidas por los usuarios. Una aplicación clásica de esta metodología requiere distintas fases entre la que se encuentran la elección del dominio del diseño, la definición del espacio semántico y de propiedades, la síntesis, la validación y la construcción del modelo. La popularización de las técnicas de inteligencia artificial, entre las que se encuentra el aprendizaje automático, ha llevado a muchos autores a utilizar estas herramientas en la fase de síntesis. En este trabajo se analizan las principales herramientas de aprendizaje automático usadas en la fase de síntesis de ingeniería kansei, así como la adecuación de su uso, en base al espacio de propiedades previamente definido.Kansei engineering is one of the main methodologies for the emotional design of products. This technique aims to relate the properties of the product or service to the sensations perceived by users. A classic application of this methodology requires different phases, among which are the choice of the product domain, the definition of the semantic space and properties, the elaboration of the synthesis, the validation and the construction of the model and validation. The popularization of artificial intelligence techniques, including machine learning, has led many authors to use these mathematical models in the synthesis phase. This paper analyses the main machine learning tools used in the synthesis phase of kansei engineering, as well as the relevance of their use, based on the property space previously described

Biopsychosocial Assessment and Ergonomics Intervention for Sustainable Living: A Case Study on Flats

This study proposes an ergonomics-based approach for those who are living in small housings (known as flats) in Indonesia. With regard to human capability and limitation, this research shows how the basic needs of human beings are captured and analyzed, followed by proposed designs of facilities and standard living in small housings. Ninety samples were involved during the study through in- depth interview and face-to-face questionnaire. The results show that there were some proposed of modification of critical facilities (such as multifunction ironing work station, bed furniture, and clothesline) and validated through usability testing. Overall, it is hoped that the proposed designs will support biopsychosocial needs and sustainability

Kansei for the Digital Era

For over 40 years, Kansei-based research and development have been conducted in Japan and other East Asian countries and these decades of research have influenced Kansei interpretation. New methods and applications, including virtual reality and artificial intelligence, have emerged since the millennium, as the Kansei concept has spread throughout Europe and the rest of the world. This paper reviews past literature and industrial experience, offering a comprehensive understanding of Kansei, the underlying philosophy, and the methodology of Kansei Engineering from the approach of psychology and physiology, both qualitatively and quantitatively. The breadth of Kansei is described by examples, emerging from both industry and academia. Additionally, thematic mapping of the state-of-the-art as well as an outlook are derived from feedback obtained from structured interview of thirty-five of the most distinguished researchers in Kansei. The mapping provides insights into current trends and future directions. Kansei is unique because it includes the consideration of emotion in the design of products and services. The paper aims at becoming a reference for researchers, practitioners, and stakeholders across borders and cultures, looking for holistic perspectives on Kansei, Kansei Engineering, and implementation methods. The novelty of the paper resides in the unification of authors amongst pioneers from different parts of the world, spanning across diversified academic backgrounds, knowledge areas and industries