Review on Emotion Recognition Databases

Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format

Emotion and mood blending in embodied artificial agents: expressing affective states in the mini social robot

Robots that are devised for assisting and interacting with humans are becoming fundamental in many applications, including in healthcare, education, and entertainment. For these robots, the capacity to exhibit affective states plays a crucial role in creating emotional bonding with the user. In this work, we present an affective architecture that grounds biological foundations to shape the affective state of the Mini social robot in terms of mood and emotion blending. The affective state depends upon the perception of stimuli in the environment, which influence how the robot behaves and affectively communicates with other peers. According to research in neuroscience, mood typically rules our affective state in the long run, while emotions do it in the short term, although both processes can overlap. Consequently, the model that is presented in this manuscript deals with emotion and mood blending towards expressing the robot's internal state to the users. Thus, the primary novelty of our affective model is the expression of: (i) mood, (ii) punctual emotional reactions to stimuli, and (iii) the decay that mood and emotion undergo with time. The system evaluation explored whether users can correctly perceive the mood and emotions that the robot is expressing. In an online survey, users evaluated the robot's expressions showing different moods and emotions. The results reveal that users could correctly perceive the robot's mood and emotion. However, emotions were more easily recognized, probably because they are more intense affective states and mainly arise as a stimuli reaction. To conclude the manuscript, a case study shows how our model modulates Mini's expressiveness depending on its affective state during a human-robot interaction scenario.The research leading to these results has received funding from the projects Robots sociales para estimulación física, cognitiva y afectiva de mayores (ROSES) RTI2018-096338-B-I00 funded by Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades and RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by "Programas de Actividades I+D en la Comunidad de Madrid" and cofunded by Structural Funds of the EU. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

Robots and autistic children: a review

In accordance with the advancement in robotics and the scholarly literature, the extents of utilizing robots for autistic children are widened and could be a promising method for individual with Autism Spectrum Disorder (ASD) treatments, where the different form of robot (humanoid, non-humanoid, animal-like, toy, and kits) can be employed effectively as a support tool to augment the learning skills and rehabilitate of the individual with Autism Spectrum Disorder (ASD). Thus, the robots were exploited for ASD children in different aspects namely; modelling, teaching, and skills practicing; testing, highlighting and evaluating; providing feedback or encouragement; join Attention; eliciting social behaviours; emotion recognition and expression; imitation; vocalization; turn-taking; and diagnostic. The related literature published recently in journals and conferences is taken into account. In this paper, we review the use of robots that help in the therapy of individuals with Autism Spectrum Disorder (ASD). The articles on using robots for autistic children rehabilitation and education which reported results of experiments on a number of participants were implicated. After looking in digital libraries under this criteria, and excluding non-related, and duplicated studies, 39 studies have been found. The findings were focused mainly on the social communication skills of autistic children and how the extent of the robots mitigate their stereotyped behaviours. Deeper research is required in this area to cover all applications of robotic on autistic children in order to design feasible and low-cost robots that ensure provide high validity

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review

Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when social robots are designed for children, and especially those with autism spectrum disorder (ASD). Social robots are based on cognitiveaffective models, which allow them to communicate with people following social behaviors and rules. However, interactions between a child and a robot may change or be different compared to those with an adult or when the child has an emotional deficit. In this study, we systematically reviewed studies related to computational models of emotions for children with ASD. We used the Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to the definition, interaction, and design of computational models supported by theoretical psychology approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children or those with ASD.This research was funded by VRIEA-PUCV, grant number 039.358/202

Affective reactions towards socially interactive agents and their computational modeling

Over the past 30 years, researchers have studied human reactions towards machines applying the Computers Are Social Actors paradigm, which contrasts reactions towards computers with reactions towards humans. The last 30 years have also seen improvements in technology that have led to tremendous changes in computer interfaces and the development of Socially Interactive Agents. This raises the question of how humans react to Socially Interactive Agents. To answer these questions, knowledge from several disciplines is required, which is why this interdisciplinary dissertation is positioned within psychology and computer science. It aims to investigate affective reactions to Socially Interactive Agents and how these can be modeled computationally. Therefore, after a general introduction and background, this thesis first provides an overview of the Socially Interactive Agent system used in this work. Second, it presents a study comparing a human and a virtual job interviewer, which shows that both interviewers induce shame in participants to the same extent. Thirdly, it reports on a study investigating obedience towards Socially Interactive Agents. The results indicate that participants obey human and virtual instructors in similar ways. Furthermore, both types of instructors evoke feelings of stress and shame to the same extent. Fourth, a stress management training using biofeedback with a Socially Interactive Agent is presented. The study shows that a virtual trainer can teach coping techniques for emotionally challenging social situations. Fifth, it introduces MARSSI, a computational model of user affect. The evaluation of the model shows that it is possible to relate sequences of social signals to affective reactions, taking into account emotion regulation processes. Finally, the Deep method is proposed as a starting point for deeper computational modeling of internal emotions. The method combines social signals, verbalized introspection information, context information, and theory-driven knowledge. An exemplary application to the emotion shame and a schematic dynamic Bayesian network for its modeling are illustrated. Overall, this thesis provides evidence that human reactions towards Socially Interactive Agents are very similar to those towards humans, and that it is possible to model these reactions computationally.In den letzten 30 Jahren haben Forschende menschliche Reaktionen auf Maschinen untersucht und dabei das “Computer sind soziale Akteure”-Paradigma genutzt, in dem Reaktionen auf Computer mit denen auf Menschen verglichen werden. In den letzten 30 Jahren hat sich ebenfalls die Technologie weiterentwickelt, was zu einer enormen Veränderung der Computerschnittstellen und der Entwicklung von sozial interaktiven Agenten geführt hat. Dies wirft Fragen zu menschlichen Reaktionen auf sozial interaktive Agenten auf. Um diese Fragen zu beantworten, ist Wissen aus mehreren Disziplinen erforderlich, weshalb diese interdisziplinäre Dissertation innerhalb der Psychologie und Informatik angesiedelt ist. Sie zielt darauf ab, affektive Reaktionen auf sozial interaktive Agenten zu untersuchen und zu erforschen, wie diese computational modelliert werden können. Nach einer allgemeinen Einführung in das Thema gibt diese Arbeit daher, erstens, einen Überblick über das Agentensystem, das in der Arbeit verwendet wird. Zweitens wird eine Studie vorgestellt, in der eine menschliche und eine virtuelle Jobinterviewerin miteinander verglichen werden, wobei sich zeigt, dass beide Interviewerinnen bei den Versuchsteilnehmenden Schamgefühle in gleichem Maße auslösen. Drittens wird eine Studie berichtet, in der Gehorsam gegenüber sozial interaktiven Agenten untersucht wird. Die Ergebnisse deuten darauf hin, dass Versuchsteilnehmende sowohl menschlichen als auch virtuellen Anleiterinnen ähnlich gehorchen. Darüber hinaus werden durch beide Instruktorinnen gleiche Maße von Stress und Scham hervorgerufen. Viertens wird ein Biofeedback-Stressmanagementtraining mit einer sozial interaktiven Agentin vorgestellt. Die Studie zeigt, dass die virtuelle Trainerin Techniken zur Bewältigung von emotional herausfordernden sozialen Situationen vermitteln kann. Fünftens wird MARSSI, ein computergestütztes Modell des Nutzeraffekts, vorgestellt. Die Evaluation des Modells zeigt, dass es möglich ist, Sequenzen von sozialen Signalen mit affektiven Reaktionen unter Berücksichtigung von Emotionsregulationsprozessen in Beziehung zu setzen. Als letztes wird die Deep-Methode als Ausgangspunkt für eine tiefer gehende computergestützte Modellierung von internen Emotionen vorgestellt. Die Methode kombiniert soziale Signale, verbalisierte Introspektion, Kontextinformationen und theoriegeleitetes Wissen. Eine beispielhafte Anwendung auf die Emotion Scham und ein schematisches dynamisches Bayes’sches Netz zu deren Modellierung werden dargestellt. Insgesamt liefert diese Arbeit Hinweise darauf, dass menschliche Reaktionen auf sozial interaktive Agenten den Reaktionen auf Menschen sehr ähnlich sind und dass es möglich ist diese menschlichen Reaktion computational zu modellieren.Deutsche Forschungsgesellschaf