Filtered by themeAffective computing24 papersclear ✕

A Novel Emotion-Aware Networking Model for Enhanced User Experience in 5G networksViktor Stoynov · 2025The emergence of 5G networks has considerably broadened the capabilities and features of mobile networks. A notable development in this area is the incorporation of emotional intelligence into mobile networks, leading to the proposal of the Emotion-aware Networking Paradigm (ENP) in this paper. ENP strives to equip mobile networks with the ability to identify and understand user emotions and adapt services accordingly. This paper introduces a model for implementing ENP in 5G mobile networks, called the Emotion-aware Networking Model (ENM), detailing its structure, functions, and main characteristics. This paper also explores the challenges and prospects related to ENM and offers examples of potential use cases. Additionally, this paper presents the Emotion-Aware Service and User Profile Management Function (EASUPMF) for ENM, designed to unleash ENM's potential by managing user profiles and delivering personalized emotional services based on emotion recognition. Several new Key Performance Indicators (KPIs) are also established to assess ENM's performance, such as Emotional Diversity, Emotional Alignment, and Emotional Intensity. Our research indicates that ENM could transform the mobile network sector and enhance user experiences by delivering tailored services grounded in user emotions.

UX of AI & PersonalizationResearch Papers

Utilizing emotion recognition technology to enhance user experience in real- timeYuanyuan Xu · 2024In recent years, advancements in human-computer interaction (HCI) have led to the emergence of emotion recognition technology as a crucial tool for enhancing user engagement and satisfaction. This study investigates the application of emotion recognition technology in real-time environments to monitor and respond to users’ emotional states, creating more personalized and intuitive interactions. The research employs convolutional neural networks (CNN) and long short-term memory networks (LSTM) to analyze facial expressions and voice emotions. The experimental design includes an experimental group that uses an emotion recognition system, which dynamically adjusts learning content based on detected emotional states, and a control group that uses a traditional online learning platform. The results show that real-time emotion monitoring and dynamic content adjustments significantly improve user experiences, with the experimental group demonstrating better engagement, learning outcomes, and overall satisfaction. Quantitative results indicate that the emotion recognition system reduced task completion time by 14.3%, lowered error rates by 50%, and increased user satisfaction by 18.4%. These findings highlight the potential of emotion recognition technology to enhance user experiences. However, challenges such as the complexity of multimodal data integration, real-time processing capabilities, and privacy and data security issues remain. Addressing these challenges is crucial for the successful implementation and widespread adoption of this technology. The paper concludes that emotion recognition technology, by providing personalized and adaptive interactions, holds significant promise for improving user experience and offers valuable insights for future research and practical applications.

UX of AI & PersonalizationResearch Papers

Optimizing Emotion Classification from EEG Signals: A Comparative Analysis of Optimization Techniques on the DEAP DatasetJanki Broker · 2024Electroencephalogram (EEG) signals for emotion classification have gained significant attention in recent times due to its implicit applications in varied fields, including human- computer interaction, affective computing, and substantiated counseling sessions. This study set out to explore the effectiveness of convolutional neural networks (CNNs) in classifying emotions using the DEAP dataset, a publicly available repository of EEG recordings and corresponding emotional annotations by experimenting with different optimizers and loss functions, like "Adamax" and "binary_crossentropy,", etc. By carefully manipulating these optimizers, the study aims to examine and determine the optimal combination with the highest classification accuracy. The findings contribute valuable insights into fine- tuning CNN models for emotion classification based on hyperparameters, paving the way for more robust and effective applications such as personalized diagnosis for counseling, stress- busting techniques and so on. The promising result of this research shows that the “Adamax” optimizer outperforms other optimizers by 82.98 % of the accuracy of classification.

UX of AI & PersonalizationResearch Papers

Emoticontrol : Emotions-based Control of User-Interfaces AdaptationsKARTHIK VAIDHYANATHAN, IIIT Hyderaba · 2023Emotions are integral to human nature, and their existence, duration, and evolution could lead to specific behaviors. If emotions and behaviors are ignored in the design of socio-technical systems, they will fail or cause discomfort. User interfaces (UIs) are elements of interactive systems able to trigger or moderate emotions. UIs are increasingly designed adaptive to users' various characteristics, intending to improve their satisfaction, performance, and decisions. However, previous adaptation supervising approaches are not effectively adopted in real life since they neglect the dynamic behaviors of humans or systems. This paper proposes Emoticontrol, a quality-driven approach to adapting UIs to users' emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users' enhanced quality of experience (QoE). The approach also considers improving the software quality of service (QoS) by designing software architecture alternatives. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in evacuation training. By taking contextual input of the users' basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally controlled. We consider software performance a crucial QoS; thus, we adopt and test architectures that facilitate an acceptable level of performance. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other UI adaptation techniques.

UX of AI & PersonalizationResearch Papers

User Interface and Architecture Adaption Based onMahyar T. Moghaddam∗, Mina Alipour et al. · 2023This paper shows how emotions and behavior considerations in socio-technical systems lead to high-quality self-adaptations, both at application and architecture levels. In our approach, an interactive control system assesses the reconfigurations that enhance the quality of service (QoS) while considering humans’ quality of experience (QoE). We use a Model-Free Reinforcement Learning (MFRL) approach to self- adapt user interfaces (UIs) to users’ emotions. The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ QoE, i.e., task comple- tion and satisfaction. If the control system detects a drop in QoS in emotion-based adaptations or other functions, another level of adaptation reconfigures the architecture towards better quality. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in such potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition and their mobility behavior, the application intelligently adapts its UI to quickly lead people to safe areas while keeping them emotionally stable. In addition to UI adaptation, the system is capable of architecture-level adaptations to decrease response time if required. The evaluation process confirms the efficiency of the MFRL in iterations, as well as compared to other possible UI adaptation techniques. The emerging results also show that architecture-level adaptations positively impact the system performance and users’ emotions and performance. Index Terms—Software Architecture, Emotions, Behaviors, Reinforcement Learning, User Interface, Emergency.

UX of AI & PersonalizationResearch Papers

Toward Changing Users behavior with Emotion-based Adaptive SystemsMina Alipour · 2023Interactive computer systems’ designers emphasize the importance of considering humans, their emotions, and behaviors as first-class entities. Emotions are integral parts of human nature, and ignor- ing that can lead the interactive systems to failure, low quality, or discomfort. User interfaces (UIs) are increasingly becoming adap- tive to users’ various characteristics, intending to improve users’ satisfaction, performance, and decisions. However, the previous approaches proposed for supervising such adaptations are not effec- tively adopted in real-life problems. This paper proposes the novel approach to adapting UIs to users’ emotions using Model-Free Re- inforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ task completion and satisfaction. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while arousing target emotions. The research includes lit- erature analysis, surveys, and further adopting an iterative process in implementation and experimentation. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other possible UI adaptation techniques, i.e., rule-based and sequential adaptation.

UX of AI & PersonalizationResearch Papers

User Feedback Design in AI-Driven Mood Tracker Mobile AppsHsi Yuan Chu, Yvette Shen(B) · 2022AI-driven civilian healthcare systems can offer personalized mental health care to more people. When users provide feedback to the system, they can influence the results of the AI models and improve their user experiences over time. In this study, we investigate feedback designs in mood tracker apps and identify feedback implementation opportunities through the lens of user expe- rience and user interface design. We reviewed the feedback designs on popular mood tracker apps and discovered that the feedback mechanisms were not reg- ularly implemented in all stages of tracking moods. To explore feedback design opportunities in line with user actions, user mindsets, and user emotions, a journey map was developed to visualize the interaction with a mood tracker app from the user perspective. Based on the journey map, a mood tracker app prototype was created for the purpose of user testing. The preliminary user testing results vali- dated some feedback design recommendations that highlight the human-centered design approach of crafting user experiences that leverage AI.

UX of AI & PersonalizationResearch Papers

An Emotion-Oriented Problem Space for UIMina Alipour · 2021Emotions significantly affect the human interaction process with computers. They constitute a dynamic user’s charac- teristic that can be crucial for adapting user interfaces (UI) to the user’s context. However, their impact on UI adaptation concepts and techniques is not well understood. This paper proposes a problem space that studies the design dimensions to be considered in UI adaptation to emotions. In particular, this emotion-oriented problem space for UI adaptation, highlights the temporal aspects of emotions. It is obtained from two literature reviews realized to identify the gaps in existing problem spaces and the potentials of including emotional aspects inside them. The proposed problem space can benefit all adaptive systems that aim to improve users’ and system’s performance based on users’ reactions to the system. Index Terms—User interface adaptation, Problem space, Emo- tion adaptation,Temporal adaptation.

UX of AI & PersonalizationResearch Papers

Seemo: A Computational Approach to See EmotionsZhe Liu, Anbang Xu et al. · 2018Successful human interactions are based on becoming aware of other’s emotion and making adaptations accordingly. However, understanding emotion is a complex task that has generated countless debates among researchers over the past decades. The abstractive nature of human emotion highlights the need for a new data-driven approach that can better describe and compare across fine-grained emotional states. In this study, we propose Seemo, a novel neural embedding framework, which allows us to map human emotions into vector space representations. Seemo is trained using Twitter data and is evaluated on two fundamental use cases in traditional emotion research: determining the underlying dimensions of emotions and identifying the set of basic emotions. The evaluation reveals that on both tasks Seemo can generate results consistent with the mainstream theories. Results also show that the vector space representation of Seemo can effectively decode the important relationships between emotions that were usually not explicitly presented. Author Keywords Emotion; affective computing; social computing; computational social science ACM Classification Keywords H.5.2. Information Interfaces and Presentation: Group and Organization Interfaces

UX of AI & PersonalizationResearch Papers

Smartphone Accelerometer Data used for Detecting Human EmotionsAndreas Færøvig Olsen, Jim Torresen · 2016The paper outlines work on the classification of emotions using smartphone accelerometer data. Such classi- fication can be used, in conjunction with other methods of emotion detection, to adapt services to the user’s emotional state. The data is collected from individuals who have been carrying their phone in a pocket while walking. An Android app was developed in order to monitor the smartphone accelerometer of the individuals who participated in the study and occasionally requested them to judge and submit their emotional state. This way, data is collected from a natural environment rather than a laboratory setting. The recorded data is then processed and used to train different classifiers to be compared. The machine learning algorithms decision tree, support vector machine and multilayer perceptron are used for this purpose. Emotions are classified in two dimensions: pleasantness and arousal (activation). While the recognition rate for the arousal dimension is promising at 75%, pleasantness is harder to predict, with a recognition rate of 51%. These findings indicate that by only analyzing accelerometer data recorded from a smartphone, it is possible to make predictions of a person’s state of activation.

UX of AI & PersonalizationResearch Papers

Affective modelling of users in HCI using EEGJyotish Kumara, Jyoti kumar · 2016Emotions have potential to play a role in HCI which is primarily dominated by cognitive measures. Human physiological communication channels are dominated by emotions. Emotion affects several human activities like communication, learning, decision making, cognition, perception etc. Further, as emotions are difficult to interpret and hard to measure, technologists and designers have been struggling to incorporate them in design and technology. On the other hand, advancement of technology has both necessitated and enabled us to understand emotions and put them to use in contexts like human computer interaction. This study reports an attempt to model emotions by means of electroencephalography (EEG). Video stimuli of four representative basic emotions based on Navarasa theory of Ancient Indian treatise called Natya Shastra were shown to participants and EEG data was collected. Power spectrum analysis of EEG signals associated with emotions was done. Further, the EEG analysis findings were compared with the subject’s self-reports about their emotional states during the experiment. EEG results have shown significantly consistent frequency patterns across the brain lobes for a given emotion. This study suggests that human emotions can be modeled for use in HCI either as an affect assessment tool or for affect based intelligent interactions. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Scientific Committee of IHCI 2015.

UX of AI & PersonalizationResearch Papers

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and FeaturesClaude Frasson, Nathan C. Hall · 2016A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven ap- proaches to enhance learners’ experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic’s breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion- aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and Int J Artif Intell Educ (2017) 27:268–297 DOI 10.1007/s40593-016-0126-8 Note. This manuscript is based on an extended version of: Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N.C. (2015a). An integrated emotion-aware framework for intelligent tutoring systems. In C. Conati & N. Heffernan (Eds.), Lectures Notes in Artificial Intelligence: Vol. 9112. Artificial Intelligence in Education (pp. 620-624). Switzerland: Springer. * Jason M. Harley jharley1@ualberta.ca 1 Educational Psychology, University of Alberta, 6-102 Education North, Edmonton, AB T6G 2G5, Canada 2 Educational and Counselling Psychology, McGill University, 3700 McTavish Street 614, Montréal, QC H3A 1Y2, Canada 3 Computer Science and Operations Research, Université de Montréal, 2920 Chemin de la Tour, Pavillon André-Aisenstadt 2194, Montréal, QC H3C 3J7, Canada regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D’Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can fos

UX of AI & PersonalizationResearch Papers

1 Fear, Emotion, and ScienceComputers are beginning to acquire the ability to ex- press and recognize affect, and may soon be given the ability to “have emotions.” The essential role of emotion in both human cognition and perception, as demonstrated by recent neurological studies, indi- cates that affective computers should not only pro- vide better performance in assisting humans, but also might enhance computers’ abilities to make de- cisions. This paper presents and discusses key issues in “affective computing,” computing that relates to, arises from, or influences emotions. Models are sug- gested for computer recognition of human emotion, and new applications are presented for computer- assisted learning, perceptual information retrieval, arts and entertainment, and human health and inter- action. Affective computing, coupled with new wear- able computers, will also provide the ability to gather new data necessary for advances in emotion and cog- nition theory. 1 Fear, Emotion, and Science Nothing in life is to be feared. It is only to be under- stood. – Marie Curie Emotions have a stigma in science; they are believed to be inherently non-scientific. Scientific principles are derived from rational thought, logical arguments, testable hypotheses, and repeatable experiments. There is room alongside science for “non-interfering” emotions such as those involved in curiosity, frustration, and the pleasure of discovery. In fact, much scien- tific research has been prompted by fear. Nonetheless, the role of emotions is marginalized at best. Why bring “emotion” or “affect” into any of the deliberate tools of science? Moreover, shouldn’t it be completely avoided when considering properties to design into computers? After all, computers control significant parts of our lives – the phone system, the stock market, nuclear power plants, jet landings, and more. Who wants a computer to be able to “feel angry” at them? To feel contempt for any living thing? In this essay I will submit for discussion a set of ideas on what I call “affective computing,” computing that relates to, arises from, or influences emotions. This will need some further clari- fication which I shall attempt below. I should say up front that I am not proposing the pursuit of computerized cingulotomies1 or even into the business of building “emotional computers”. 1The making of small wounds in the ridge of the limbic sys- tem known as the cingulate gyrus, a surgical procedure to aid severely depressed patients. Nor will I propose answers

Emotional DesignResearch Papers

The Future of Emotion in Human-Computer InteractionGreg Wadley · 2022Emotion has been studied in HCI for two decades, with specifc tra­ ditions interested in sensing, expressing, transmitting, modelling, experiencing, visualizing, understanding, constructing, regulating, manipulating or adapting to emotion in human-human and human- computer interactions. This CHI 2022 workshop on the Future of Emotion in Human-Computer Interaction brings together in­ terested researchers to take stock of research on emotion in HCI to-date and to explore possible futures. Through group discussion and collaborative speculation we will address questions such as: What are the relationships between digital technology and human emotion? What roles does emotion play in HCI research? How should HCI researchers conceptualize emotion? When should HCI researchers use interdisciplinary theories of emotion or create new theory? Can specifc emotions be designed for, and where is this knowledge likely to be applied? What are the implications of emo­ tion research for design, ethics and wellbeing? What is the future of emotion in human-computer interaction? CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI).

Emotional DesignResearch Papers

Making Sense of Emotion-Sensing: Workshop on Quantifying Human EmotionsBenjamin Tag · 2021The global pandemic and the uncertainty if and when life will return to normality have motivated a series of studies on human mental health. This research has elicited evidence for increasing numbers of anxiety, depression, and overall impaired mental well-being. But, the global COVID-19 pandemic has also created new opportunities for research into quantifying human emotions: remotely, contact- less, in everyday life. The ubiquitous computing community has long been at the forefront of developing, testing, and building user- facing systems that aim at quantifying human emotion. However, rather than aiming at more accurate sensing algorithms, it is time to critically evaluate whether it is actually possible and in what ways it could be beneficial for technologies to be able to detect user emotions. In this workshop, we bring together experts from the fields of Ubiquitous Computing, Human-Computer Interaction, and Psychology to - long-overdue - merge their expertise and ask the fundamental questions: how do we make sense of emotion-sensing, can and should we quantify human emotions? CCS CONCEPTS • Human-centered computing →Human computer interac- tion (HCI); Ubiquitous and mobile computing; • Applied com- puting →Psychology. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. UbiComp-ISWC ’21 Adjunct, September 21–26, 2021, Virtual, USA © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-8461-2/21/09...$15.00 https://doi.org/10.1145/3460418.3479272

Emotional DesignResearch Papers

NeuroDesign: Making decisions and solving problems through understanding of the human brain2020This paper introduces an emerging research field called NeuroDe- sign, which combines psychology, neuroscience, computer science, and design. It applies the cutting-edge cross-domain research methods to collect the designer’s physiological data and psychological data, which has essential experimental support significance for this research field. Studies on NeuroDe- sign are based on neuroscience and use psychology methods and computer models to study design-related activities. The combination of neuroscience and design is an emerging field, and researching in this field has considerable significance.

Emotional DesignResearch Papers

Emotional MachinesJordi Vallverdú · 2019Artificial Intelligence (AI) is the motor that fuels the most profound revolution in the history of humankind. The new era of information society [10], even called "the Fourth Revolution" [8], has produced deep changes into sciences, economies, and societies [9]. This revolution is not only about formal heuristics, or the called "algorithmic society", but also is explicitly related to the creation of strong interactions between humans and machines. Consequently, these machines must be able to deal with the most intrinsic feature of human nature: emotions. If emotions had been historically considered as disturbing elements of human rationality, the neurological revolution and new experimental tools such as magnetic scanning of the brain (with great success for fMRI) made possible a complete Copernican turn into the evaluation of the role of emotions in cognitive frameworks [13,14]. This process led experts in natural and artificial cognition to consider the necessity of including emotions into their models [37]. Affective computing or social robotics [7,44] increased not only the necessity of studies about how to improve the emotional interaction between humans and machines [57,59] but also how to design cognitive architectures which included biomimetic elements related with emo-

Emotional DesignResearch Papers

Designing User Interfaces in Emotionally-Sensitive Applications2018A method for analysing emotion and motivation for design of affective user interfaces is described. Theories from psychology of emotion and motivation are applied in user-centred design to analyse desired influences on users, and plan appropriate UI features, based on a scenario-based approach that analyses affective situations. The method informs design of multimedia and agent-based user interfaces for persuasive technology applications and domains where the user interface may have emotive effects. Use of method is illustrated with a case study in health informatics for a persuasive technology application.

Emotional DesignResearch Papers

Emotion detection: a technology review2017Emotion detection has become one of the most important aspects to consider in any project related to Affective Computing. Due to the almost endless applications of this new discipline, the development of emotion detection technologies has brought up as a quite profitable opportunity in the corporate sector. Many start-up enterprises have emerged in the last years, dedicated almost exclusively to a specific type of emotion detection technology. In this paper, we present a thorough review of current technologies to detect human emotions. To this end, we explore the different sources from which emotions can be read, along with existing technologies developed to recognize them. We also explore some application domains in which this technology has been applied. This survey has let us identify the strengths and shortcomings of current technology for emotion detection. We conclude the survey highlighting the aspects that requires further research and development. CCS CONCEPTS • Human-Centered Computing → Interaction Design.

Emotional DesignResearch Papers

Comparison of selected off-the-shelf solutions forGrzegorz Brodny, Agata Kołakowsk et al. · 2016The paper concerns accuracy of emotion recognition from facial expressions. As there are a couple of ready off-the-shelf solutions available in the market today, this study aims at practical evaluation of selected solutions in order to provide some insight into what potential buyers might expect. Two solutions were compared: FaceReader by Noldus and Xpress Engine by QuantumLab. The performed evaluation revealed that the recognition accuracies differ for photo and video input data and therefore solutions should be matched to the specificity of the application domain.

Emotional DesignResearch Papers

EMOTION MONITORING – VERIFICATION OF PHYSIOLOGICAL CHARACTERISTICS MEASUREMENT PROCEDURES Agnieszka Landowska2014This paper concerns measurement procedures on an emotion monitoring stand designed for tracking human emotions in the Human-Computer Interaction with physiological characteristics. The paper addresses the key problem of physiological measurements being disturbed by a motion typical for human-computer interaction such as keyboard typing or mouse movements. An original experiment is described, that aimed at practical evaluation of measurement procedures performed at the emotion monitoring stand constructed at GUT. Different locations of sensors were considered and evaluated for suitability and measurement precision in the Human- Computer Interaction monitoring. Alternative locations (ear lobes and forearms) for skin conductance, blood volume pulse and temperature sensors were proposed and verified. Alternative locations proved correlation with traditional locations as well as lower sensitiveness to movements like typing or mouse moving, therefore they can make a better solution for monitoring the Human-Computer Interaction.

Emotional DesignResearch Papers

Emotion recognition and its applications2014The paper proposes a set of research scenarios to be applied in four domains: software engineering, website customization, education and gaming. The goal of applying the scenarios is to assess the possibility of using emotion recognition methods in these areas. It also points out the problems of defining sets of emotions to be recognized in different applications, representing the defined emotional states, gathering the data and training. Some of the scenarios consider possible reactions of an affect-aware system and its impact on users.

Emotional DesignResearch Papers

Affective computing: challenges2003A number of researchers around the world have built machines that recognize, express, model, communicate, and respond to emotional information, instances of ‘‘affective computing.’’ This article raises and responds to several criticisms of affective computing, articulating state-of-the art research challenges, especially with respect to affect in human- computer interaction. r 2003 Elsevier Science Ltd. All rights reserved.

Emotional DesignResearch Papers

Emotion in Human–Computer InteractionScott Brave · 2002Emotion is a fundamental component of being human. Joy, hate, anger, and pride, among the plethora of other emotions, motivate action and add meaning and richness to virtually all human experience. Traditionally, human–computer interaction (HCI) has been viewed as the "ultimate" exception; users must discard their emotional selves to work efficiently and rationality with computers, the quintessentially unemotional artifact. Emotion seemed at best marginally relevant to HCI and at worst oxymoronic. Recent research in psychology and technology suggests a different view of the relationship between humans, computers, and emotion. After a long period of dormancy and confusion, there has been an explosion of research on the psychology of emotion (Gross, 1999). Emotion is no longer seen as limited to the occasional outburst of fury when a computer crashes inexplicably, excitement when a video game character leaps past an obstacle, or frustration at an incomprehensible error message. It is now understood that a wide range of emotions plays a critical role in every computer-related, goal-directed activity, from developing a three-dimensional (3D) CAD model and running calculations on a spreadsheet, to searching the Web and sending an e-mail, to making an online purchase and playing solitaire. Indeed, many psychologists now argue that it is impossible for a person to have a thought or perform an action without engaging, at least unconsciously, his or her emotional systems (Picard, 1997b).

Emotional DesignResearch Papers