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Affective Computing
Open accessAbstract
According to Rosalind Picard, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions. The latest scientific findings indicate that emotions play an essential role in decision making, perception, learning, and more—that is, they influence the very mechanisms of rational thinking. Not only too much, but too little emotion can impair decision making. According to Rosalind Picard, if we want computers to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have and express emotions. Part 1 of this book provides the intellectual framework for affective computing. It includes background on human emotions, requirements for emotionally intelligent computers, applications of affective computing, and moral and social questions raised by the technology. Part 2 discusses the design and construction of affective computers. Although this material is more technical than that in Part 1, the author has kept it less technical than typical scientific publications in order to make it accessible to newcomers. Topics in Part 2 include signal-based representations of emotions, human affect recognition as a pattern recognition and learning problem, recent and ongoing efforts to build models of emotion for synthesizing emotions in computers, and the new application area of affective wearable computers.
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“In contrast, the goal of our system was to use on-body sensors with EDA and electrocardiogram (EKG), since these sensors have been shown to be reliable for emotion detection [14,17,18].”
From Food and Mood: Just-in-Time Support for Emotional Eating (2013) · cites this paper as [18]
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Mauri, M., Cipresso, P., Balgera, A., Villamira, M., & Riva, G. (2011) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2010.0377 · Cited by 273
Modeling emotions and other motivations in synthetic agents
Velásquez, J. D. (1997) · Cited by 274
EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines
Wang, X., Nie, D., & Lu, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24955-6_87 · Cited by 277
Cyr, D., & Trevor-Smith, H. B. (2004) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20075 · Cited by 280
Authenticity in the age of digital companions
Turkle, S. (2007) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.8.3.11tur · Cited by 272
On the biology of technostress
Riedl, R. (2012) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/2436239.2436242 · Cited by 304
Emotion Recognition in Immersive Virtual Reality: From Statistics to Affective Computing
Marín‐Morales, J., Llinares, C., Guixeres, J., & Alcañíz, M. (2020) · Sensors · doi:10.3390/s20185163 · Cited by 298
Interactivity in the Context of Designed Experiences
Heeter, C. (2000) · Journal of Interactive Advertising · doi:10.1080/15252019.2000.10722040 · Cited by 269
A continuous and objective evaluation of emotional experience with interactive play environments
Mandryk, R. L., Atkins, M. S., & Inkpen, K. M. (2006) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124926 · Cited by 269
Montoyo, A., Martínez-Barco, P., & Balahur, A. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.05.022 · Cited by 269
Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech
Castellano, G., Kessous, L., & Caridakis, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85099-1_8 · Cited by 267
de Rosis, F., Pélachaud, C., Poggi, I., Carofiglio, V., & Carolis, B. N. D. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00020-x · Cited by 274
Doherty, K., & Doherty, G. J. (2018) · ACM Computing Surveys · doi:10.1145/3234149 · Cited by 299
Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood
de Gennaro, M., Krumhuber, E. G., & Lucas, G. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03061 · Cited by 265
Tangible products: redressing the balance between appearance and action
Djajadiningrat, T., Wensveen, S. S. A. G., Frens, J. W., & Overbeeke, K. (2004) · Personal and Ubiquitous Computing · doi:10.1007/s00779-004-0293-8 · Cited by 264
Big data analytics: Computational intelligence techniques and application areas
Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.03.024 · Cited by 271
Lopatovska, I., & Arapakis, I. (2010) · Information Processing & Management · doi:10.1016/j.ipm.2010.09.001 · Cited by 279
Technology-driven service strategy
Huang, M.-H., & Rust, R. T. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0545-6 · Cited by 263
Making epistemological trouble: Third-paradigm HCI as successor science
Harrison, S., Sengers, P., & Tatar, D. G. (2011) · Interacting with Computers · doi:10.1016/j.intcom.2011.03.005 · Cited by 279
Dynamic Difficulty Adjustment in Computer Games Through Real-Time Anxiety-Based Affective Feedback
Liu, C., Agrawal, P., Sarkar, N., & Chen, S. (2009) · International Journal of Human-Computer Interaction · doi:10.1080/10447310902963944 · Cited by 259
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.7 · Cited by 260
Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me
Gilleade, K. M., Dix, A. J., & Allanson, J. (2005) · doi:10.26503/dl.v2005i1.229 · Cited by 260
Kessous, L., Castellano, G., & Caridakis, G. (2010) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0025-5 · Cited by 256
Emotions in human and artificial intelligence
Martı́nez-Miranda, J., & Aldea, A. (2004) · Computers in Human Behavior · doi:10.1016/j.chb.2004.02.010 · Cited by 264
Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents
Cassell, J., & Bickmore, T. (2003) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1024026532471 · Cited by 253
Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
Poria, S., Gelbukh, A. F., Hussain, A., Howard, N., Das, D., & Bandyopadhyay, S. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.4 · Cited by 250
Relational agents : effecting change through human-computer relationships
Bickmore, T. (2003) · Cited by 252
Arbib, M. A., & Fellous, J. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.10.004 · Cited by 257
Improving automotive safety by pairing driver emotion and car voice emotion
Nass, C., Jonsson, I.-M., Harris, H., Reaves, B., Endo, J., Brave, S., & Takayama, L. (2005) · CHI '05 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1056808.1057070 · Cited by 249
Machine Analysis of Facial Expressions
Pantic, M., & Stewart, M. (2007) · Face Recognition · doi:10.5772/4847 · Cited by 248
Emotion recognition in speech signal: experimental study, development, and application
Petrushin, V. A. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-791 · Cited by 247
A dataset of continuous affect annotations and physiological signals for emotion analysis
Sharma, K., Castellini, C., van den Broek, E. L., Albu‐Schäffer, A., & Schwenker, F. (2019) · Scientific Data · doi:10.1038/s41597-019-0209-0 · Cited by 246
Pedagogical Agent Design: The Impact of Agent Realism, Gender, Ethnicity, and Instructional Role
Baylor, A. L., & Kim, Y. A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30139-4_56 · Cited by 245
Looije, R., Neerincx, M. A., & Cnossen, F. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.08.007 · Cited by 243
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
Sixteen facial expressions occur in similar contexts worldwide
Cowen, A., Keltner, D. J., Schroff, F., Jou, B., Adam, H., & Prasad, G. (2020) · Nature · doi:10.1038/s41586-020-3037-7 · Cited by 247
Technostress from a Neurobiological Perspective
Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2012) · Business & Information Systems Engineering · doi:10.1007/s12599-012-0207-7 · Cited by 277
Emotion recognition from text using semantic labels and separable mixture models
Wu, C., Chuang, Z.-J., & Lin, Y.-C. (2006) · ACM Transactions on Asian Language Information Processing · doi:10.1145/1165255.1165259 · Cited by 244
Speech emotion recognition: Features and classification models
Chen, L., Mao, X., Xue, Y., & Cheng, L. L. (2012) · Digital Signal Processing · doi:10.1016/j.dsp.2012.05.007 · Cited by 246
Effective affective user interface design in games
Johnson, D. M., & Wiles, J. (2003) · Ergonomics · doi:10.1080/00140130310001610865 · Cited by 239
Boehner, K., DePaula, R., Dourish, P., & Sengers, P. (2005) · Decennial Aarhus Conference · doi:10.1145/1094562.1094570 · Cited by 239
Shute, V. J., & Zapata‐Rivera, D. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139049580.004 · Cited by 254
Wearable sensor badge and sensor jacket for context awareness
Farringdon, J., Moore, A. J., Tilbury, N., Church, J., & Biemond, P. D. (2003) · International Symposium on Wearable Computers · doi:10.1109/iswc.1999.806681 · Cited by 239
Affective computing in education: A systematic review and future research
Yadegaridehkordi, E., Noor, N. F. B. M., Ayub, M. N. B., Affal, H., & Hussin, N. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103649 · Cited by 262
Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises
Bone, D., Goodwin, M. S., Black, M. P., Lee, C.-C., Audhkhasi, K., & Narayanan, S. S. (2014) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-014-2268-6 · Cited by 239
Towards an intelligent framework for multimodal affective data analysis
Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.005 · Cited by 236
Social Navigation of Information Space
Munro, A., Höök, K., & Benyon, D. (1999) · Computer supported cooperative work · doi:10.1007/978-1-4471-0837-5 · Cited by 235
Nudge Nudge Wink Wink: Elements of Face-to-Face Conversation for Embodied Conversational Agents
Cassell, J. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/2697.003.0002 · Cited by 234
Recognition of facial expressions and measurement of levels of interest from video
Yeasin, M., Bullot, B., & Sharma, R. (2006) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2006.870737 · Cited by 234
Affective multimodal human-computer interaction
Pantic, M., Sebe, N., Cohn, J. F., & Huang, T. (2005) · Proceedings of the 13th annual ACM international conference on Multimedia · doi:10.1145/1101149.1101299 · Cited by 234
Affect, Meta-Affect, and Mathematical Belief Structures
Goldin, G. A. (2002) · Springer eBooks · doi:10.1007/0-306-47958-3_4 · Cited by 249
Boredom, engagement and anxiety as indicators for adaptation to difficulty in games
Chanel, G., Rebetez, C., Bétrancourt, M., & Pun, T. (2008) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/1457199.1457203 · Cited by 233
Modeling Player Experience for Content Creation
Pedersen, C. R., Togelius, J., & Yannakakis, G. N. (2010) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2010.2043950 · Cited by 233
Affective state estimation for human–robot Interaction
Kulic, D., & Croft, E. A. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.904899 · Cited by 230
Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics
Valenza, G., Citi, L., Lanatà, A., Scilingo, E. P., & Barbieri, R. (2014) · Scientific Reports · doi:10.1038/srep04998 · Cited by 234
Kim, J., Lee, J.-E., & Choi, D.-S. (2003) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2003.06.002 · Cited by 243
Extracting moods from pictures and sounds: towards truly personalized TV
Hanjalić, A. (2006) · IEEE Signal Processing Magazine · doi:10.1109/msp.2006.1621452 · Cited by 227
Talbot, J., Lee, B., Kapoor, A., & Tan, D. S. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518895 · Cited by 231
Influencing group participation with a shared display
DiMicco, J. M., Pandolfo, A., & Bender, W. R. (2004) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/1031607.1031713 · Cited by 224
A hybrid approach to the sentiment analysis problem at the sentence level
Appel, O., Chiclana, F., Carter, J., & Fujita, H. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.040 · Cited by 225
A survey on empathetic dialogue systems
Ma, Y., Nguyen, K. L., Xing, F., & Cambria, E. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.011 · Cited by 230
Intrinsic Motivation and Reinforcement Learning
Barto, A. G. (2012) · Springer eBooks · doi:10.1007/978-3-642-32375-1_2 · Cited by 243
Toward computers that recognize and respond to user emotion
Picard, R. W. (2000) · IBM Systems Journal · doi:10.1147/sj.393.0705 · Cited by 222
Self-citationPedagogical agents as learning companions: the impact of agent emotion and gender
Kim, Y., Baylor, A. L., & Shen, E. (2007) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2006.00210.x · Cited by 228
Body Movements for Affective Expression: A Survey of Automatic Recognition and Generation
Karg, M. E., Samadani, A.-A., Gorbet, R., Kühnlenz, K., Hoey, J., & Kulić, D. (2013) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2013.29 · Cited by 224
Transformers for EEG-Based Emotion Recognition: A Hierarchical Spatial Information Learning Model
Wang, Z., Wang, Y., Hu, C., Yin, Z., & Song, Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3144317 · Cited by 228
Inkpen, D. Z., & Association for Computing Machinery-Digital Library (2010) · Cited by 218
McStay, A. (2020) · Big Data & Society · doi:10.1177/2053951720904386 · Cited by 217
Recognizing Human Emotional State From Audiovisual Signals
Wang, Y., & Guan, L. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.921734 · Cited by 213
Saganowski, S., Perz, B., Polak, A. G., & Kazienko, P. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3176135 · Cited by 236
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Shibata, T. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2004.835383 · Cited by 215
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Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., & Scherer, K. R. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.7 · Cited by 222
New research perspectives on Ambient Intelligence
Aarts, E., & de Ruyter, B. (2009) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-2009-0001 · Cited by 214
Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review
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Dias, J., & Paiva, A. (2005) · Lecture notes in computer science · doi:10.1007/11595014_13 · Cited by 220
Oberlander, J., & Nowson, S. (2006) · International Conference on Computational Linguistics and Annual Meeting of the Association for Computational Linguistics (COLING/ACL) · doi:10.3115/1273073.1273154 · Cited by 211
Li, C., Bao, Z., Li, L., & Zhao, Z. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.102185 · Cited by 219
Mantello, P. A., Ho, T. M., Nguyen, M., & Vuong, Q. (2021) · AI & Society · doi:10.1007/s00146-021-01290-1 · Cited by 238
Context Based Emotion Recognition Using EMOTIC Dataset
Kosti, R., Alvarez, J., Recasens, A., & Lapedriza, A. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2916866 · Cited by 211
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Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., & Šabanović, S. (2020) · doi:10.1017/9781108676649 · Cited by 211
Swarm intelligence in intrusion detection: A survey
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GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
Li, Y., Chen, J., Li, F., Fu, B., Wu, H., Ji, Y., Zhou, Y., Niu, Y., Shi, G., & Zheng, W. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3170428 · Cited by 214
Using frustration in the design of adaptive videogames
Gilleade, K. M., & Dix, A. J. (2004) · International Conference on Advances in Computer Entertainment Technology (ACE) · doi:10.1145/1067343.1067372 · Cited by 208
Ambient Intelligence: A New Multidisciplinary Paradigm
Remagnino, P., & Foresti, G. L. (2004) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2004.838456 · Cited by 203
Emotion Recognition in Context
Kosti, R., Álvarez, J. M., Recasens, A., & Lapedriza, À. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.212 · Cited by 223
Gunes, H., & Piccardi, M. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.39 · Cited by 206
Affective computing with primary and secondary emotions in a virtual human
Becker-Asano, C., & Wachsmuth, I. (2009) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-009-9094-9 · Cited by 208
Cross-Subject Multimodal Emotion Recognition Based on Hybrid Fusion
Çimtay, Y., Ekmekcioǧlu, E., & Özhan, Ş. Ç. (2020) · IEEE Access · doi:10.1109/access.2020.3023871 · Cited by 207
Recognizing Human Emotional State From Audiovisual Signals*
Wang, Y., & Guan, L. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.927665 · Cited by 202
“Neither an angel nor an ant”: Emotion as an aid to bounded rationality
Hanoch, Y. (2002) · Journal of Economic Psychology · doi:10.1016/s0167-4870(01)00065-4 · Cited by 199
Pre-collision safety strategies for human-robot interaction
Kulić, D., & Croft, E. A. (2006) · Autonomous Robots · doi:10.1007/s10514-006-9009-4 · Cited by 200
Prediction of subjective states from psychophysiology: A multivariate approach
Fairclough, S. H., & Venables, L. (2005) · Biological Psychology · doi:10.1016/j.biopsycho.2005.03.007 · Cited by 199
Affective Image Content Analysis: Two Decades Review and New Perspectives
Zhao, S., Yao, X., Yang, J., Jia, G., Ding, G., Chua, T., Schuller, B. W., & Keutzer, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3094362 · Cited by 210
Contextual music information retrieval and recommendation: State of the art and challenges
Kaminskas, M., & Ricci⋆, F. (2012) · Computer Science Review · doi:10.1016/j.cosrev.2012.04.002 · Cited by 214
Affective Computing—A Rationale for Measuring Mood With Mouse and Keyboard
Zimmermann, P., Guttormsen, S., Danuser, B., & Gomez, P. (2003) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2003.11076589 · Cited by 197
CyberGate: A Design Framework and System for Text Analysis of Computer-Mediated Communication1
Abbasi, A., & Chen, H. (2008) · MIS Quarterly · doi:10.2307/25148873 · Cited by 196
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Grimes, D. B., Tan, D. S., Hudson, S. E., Shenoy, P., & Rao, R. P. N. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357187 · Cited by 196
Coeckelbergh, M. (2011) · Ethics and Information Technology · doi:10.1007/s10676-011-9279-1 · Cited by 224
Metadata
- Authors
- Picard, R. W.
- Affiliations
- MIT Media Lab (US)
- Corresponding author
- Picard, Rosalind W.
- ORCID
- Picard, Rosalind W.
- Venue
- MIT Press
- Publisher
- The MIT Press
- Published
- 1997-09-24
- OpenAlex
- W2339343773
- Cited by
- 7,382
- References
- 0
- Citations by year
- 2026: 376 · 2025: 295 · 2024: 212 · 2023: 266 · 2022: 208 · 2021: 274 · 2020: 322 · 2019: 356 · 2018: 283 · 2017: 279 · 2016: 301 · 2015: 318 · 2014: 315 · 2013: 335 · 2012: 329
- FWCI
- 92.96
- Type
- book
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- The MIT Press eBooks (publishedVersion)
- PsycEXTRA Dataset (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- affective computing, emotion recognition, emotion models, emotion synthesis, wearable computers, human-computer interaction, decision-making, pattern recognition
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