Affective Computing is a growing multidisciplinary field encompassing computer science, engineering, psychology, education, neuroscience, and many other disciplines. It explores how affective factors influence interactions between humans and technology, how affect sensing and affect generation techniques can inform our understanding of human affect, and on the design, implementation, and evaluation of systems that intricately involve affect at their core. The Oxford Handbook of Affective Computing will help both new and experienced researchers identify trends, concepts, methodologies, and applications in this burgeouning field. The volume features 41 chapters divided into five main sections: history and theory, detection, generation, methodologies, and applications. Section One begins with a look at the makings of AC and a historical review of the science of emotion. Chapters discuss the theoretical underpinnings of AC from an interdisciplinary perspective involving the affective, cognitive, social, media, and brain sciences. Section Two focuses on affect detection or affect recognition, which is one of the most commonly investigated areas in AC. Section Three examines aspects of affect generation including the synthesis of emotion and its expression via facial features, speech, postures and gestures. Cultural issues in affect generation are also discussed. Section Four features chapters on methodological issues in AC research, including data collection techniques, multimodal affect databases, emotion representation formats, crowdsourcing techniques, machine learning approaches, affect elicitation techniques, useful AC tools, and ethical issues in AC. Finally, Section Five highlights existing and future applications of AC in domains such as formal and informal learning, games, robotics, virtual reality, autism research, healthcare, cyberpsychology, music, deception, reflective writing, and cyberpsychology.With chapters authored by world leaders in each area, The Oxford Handbook of Affective Computing is suitable for use as a textbook in undergraduate or graduate courses in AC, and will serve as a valuable resource for students, researchers, and practitioners across the globe.
Cited By
- Pan B, Hirota K, Jia Z and Dai Y (2023). A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods, Neurocomputing, 561:C, Online publication date: 7-Dec-2023.
- Cang X, Bucci P, Rantala J and MacLean K (2021). Discerning Affect From Touch and Gaze During Interaction With a Robot Pet, IEEE Transactions on Affective Computing, 14:2, (1598-1612), Online publication date: 1-Apr-2023.
- Rifah L and Zamahsari G Can Technology Replace the Teachers’ Role in Higher Education Settings? A Systematic Literature Review Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology, (217-221)
- Pinitas K, Makantasis K, Liapis A and Yannakakis G Supervised Contrastive Learning for Affect Modelling Proceedings of the 2022 International Conference on Multimodal Interaction, (531-539)
- Zhu Q, Chau A, Cohn M, Liang K, Wang H, Zellou G and Yu Z Effects of Emotional Expressiveness on Voice Chatbot Interactions Proceedings of the 4th Conference on Conversational User Interfaces, (1-11)
- Dickinson R, Semertzidis N and Mueller F Machine In The Middle: Exploring Dark Patterns of Emotional Human-Computer Integration Through Media Art CHI Conference on Human Factors in Computing Systems Extended Abstracts, (1-7)
- Broekens J Emotion The Handbook on Socially Interactive Agents, (349-384)
- Melhart D, Gravina D and Yannakakis G Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch Proceedings of the 15th International Conference on the Foundations of Digital Games, (1-10)
- Taub M, Sawyer R, Smith A, Rowe J, Azevedo R and Lester J (2020). The agency effect, Computers & Education, 147:C, Online publication date: 1-Apr-2020.
- Camacho-Morles J, Slemp G, Oades L, Pekrun R and Morrish L (2019). Relative incidence and origins of achievement emotions in computer-based collaborative problem-solving, Computers in Human Behavior, 98:C, (41-49), Online publication date: 1-Sep-2019.
- Smith K, Dennis M, Masthoff J and Tintarev N (2019). A methodology for creating and validating psychological stories for conveying and measuring psychological traits, User Modeling and User-Adapted Interaction, 29:3, (573-618), Online publication date: 1-Jul-2019.
- Howell N, Niemeyer G and Ryokai K Life-Affirming Biosensing in Public Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, (1-16)
- Howell N, Chuang J, De Kosnik A, Niemeyer G and Ryokai K (2018). Emotional Biosensing, Proceedings of the ACM on Human-Computer Interaction, 2:CSCW, (1-25), Online publication date: 1-Nov-2018.
- Murray G and Lai C Multimodal Analysis of Group Attitudes Towards Meeting Management Proceedings of the Group Interaction Frontiers in Technology, (1-6)
- Huang D, Seyed T, Li L, Gong J, Yao Z, Jiao Y, Chen X and Yang X Orecchio Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, (697-710)
- D'Mello S, Bosch N and Chen H Multimodal-multisensor affect detection The Handbook of Multimodal-Multisensor Interfaces, (167-202)
- Pérez J, Cerezo E, Gallardo J and Serón F Evaluating an ECA with a Cognitive-Affective Architecture Proceedings of the XIX International Conference on Human Computer Interaction, (1-8)
- Spaulding S Personalized Robot Tutors that Learn from Multimodal Data Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, (1781-1783)
- Bevilacqua F, Engström H and Backlund P (2018). Automated Analysis of Facial Cues from Videos as a Potential Method for Differentiating Stress and Boredom of Players in Games, International Journal of Computer Games Technology, 2018, (1), Online publication date: 1-Jan-2018.
- Barral O, Kosunen I and Jacucci G (2017). No Need to Laugh Out Loud, ACM Transactions on Computer-Human Interaction, 24:6, (1-29), Online publication date: 31-Dec-2018.
- Sawyer R, Smith A, Rowe J, Azevedo R and Lester J Enhancing Student Models in Game-based Learning with Facial Expression Recognition Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, (192-201)
- Bakhtiyari K, Ziegler J and Husain H KinRes Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, (472-475)
- Monkaresi H, Bosch N, Calvo R and D'Mello S (2017). Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate, IEEE Transactions on Affective Computing, 8:1, (15-28), Online publication date: 1-Jan-2017.
- Florea C, Florea L, Butnaru R, Bandrabur A and Vertan C (2016). Pain intensity estimation by a self-taught selection of histograms of topographical features, Image and Vision Computing, 56:C, (13-27), Online publication date: 1-Dec-2016.
- Griol D and Molina J (2016). A framework for improving error detection and correction in spoken dialog systems, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 20:11, (4229-4241), Online publication date: 1-Nov-2016.
- Mottelson A and Hornbæk K An affect detection technique using mobile commodity sensors in the wild Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, (781-792)
- Bosch N, D'mello S, Ocumpaugh J, Baker R and Shute V (2016). Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms, ACM Transactions on Interactive Intelligent Systems, 6:2, (1-26), Online publication date: 3-Aug-2016.
- Yuksel B, Oleson K, Harrison L, Peck E, Afergan D, Chang R and Jacob R Learn Piano with BACh Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, (5372-5384)
- Mone G (2015). Sensing emotions, Communications of the ACM, 58:9, (15-16), Online publication date: 24-Aug-2015.