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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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Chanel, G., Kalogianni, K., & Pun, T. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388738 · Cited by 8
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Solis, J., & Takanishi, A. (2010) · Accountability in Research · doi:10.1080/08989621.2010.523673 · Cited by 8
Data mining in emotion color with affective computing
Lee, M.-F., Chen, G.-S., Hung, J. C.-S., Lin, K.-C., & Wang, J. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2231-8 · Cited by 8
A conceptual architecture of an arduino-based social-emotional interactive system
Negru, S. (2010) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2010.5606459 · Cited by 8
Design of autonomous navigation system based on affective cognitive learning and decision-making
Zhang, H., & Liu, S. (2009) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2009.5420477 · Cited by 8
A design framework for metaheuristics
Johnson, C. G. (2008) · Artificial Intelligence Review · doi:10.1007/s10462-009-9113-x · Cited by 8
An Extension of MPML with Emotion Recognition Functions Attached
Mao, X., Li, Z., & Bao, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85483-8_30 · Cited by 8
Fuzzy-GIST for 4-emotion recognition in natural scene images
Zhang, Q., & Lee, M.-H. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596978 · Cited by 9
Stickel, C., Scerbakov, A., Kaufmann, T., & Ebner, M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89350-9_13 · Cited by 8
Bordas, J. (2014) · Virtual Reality International Conference · doi:10.1145/2617841.2620702 · Cited by 8
Cross-modal elicitation of affective experience
Mühl, C., & Heylen, D. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349455 · Cited by 8
Homogeneous Markov Chain for Modeling Emotional Interactions
Yu, D., Tang, Y., Fang, J., Zhou, Y., & Sun, M. (2008) · International Conference on Advanced Communication Technology · doi:10.1109/icact.2008.4493758 · Cited by 8
Multi-modal emotion-related data collection within a virtual earthquake emulator
Ververidis, D., Kotsia, I., Kotropoulos, C. L., & Pitas, I. (2008) · Cited by 8
Improving Accuracy and Practicality of Accelerometer- Based Hand Gesture Recognition
Mace, D., Gao, W., & Coskun, A. K. (2013) · Cited by 8
Ontology of Psychological Attributes on Color Emotions
Muramatsu, K., Togawa, T., Kojima, K., & Matsui, T. (2015) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.30.47 · Cited by 8
A novel factored POMDP model for affective dialogue management
Ren, F., Wang, Y., & Quan, C. (2016) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-162126 · Cited by 7
User Emotion Recognition Based on Multi-class Sensors of Smartphone
Shi, D.-X., Chen, X., Wei, J., & Yang, R. (2015) · International Conference on Smart Cities · doi:10.1109/smartcity.2015.116 · Cited by 7
Emotions in Adaptive Computer Technologies for Adults Improving Reading
Graesser, A., Baer, W. O., Feng, S., Walker, B., Clewley, D., Hays, D., & Greenberg, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801856-9.00001-3 · Cited by 6
Logical Modeling of Emotions for Ambient Intelligence
Adam, C., Gaudou, B., Login, D., & Lorini, E. (2011) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-61692-857-5.ch007 · Cited by 8
Affective Contextual Mobile Recommender System
Wu, C., Jia, J., Zhu, W., Chen, X., Yang, B., & Zhang, Y. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964327 · Cited by 8
Engel, F., Bond, R., Keary, A., Mulvenna, M. D., Walsh, P., Zheng, H., Wang, H., Kowohl, U., & Hemmje, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50070-6_5 · Cited by 8
Multimodal Communication in Face-to-Face Computer-Mediated Conversations
Louwerse, M. M., Benesh, N., Hoque, M. E., Jeuniaux, P. P. J. M. H., Lewis, G. A., Wu, J. Y., & Zirnstein, M. (2007) · eScholarship (California Digital Library) · Cited by 8
Scholten, M. R., Kelders, S. M., & van Gemert‐Pijnen, J. E. W. C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55134-0_4 · Cited by 8
Engagement with Artificial Intelligence through Natural Interaction Models
Feldman, S., DiPaola, S., & Yalçın, Ö. (2017) · Electronic Visualisation and the Arts London Conference · doi:10.14236/ewic/eva2017.60 · Cited by 15
Designing User Interfaces in Emotionally-Sensitive Applications
Sutcliffe, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67687-6_27 · Cited by 8
Transitions in human–computer interaction: from data embodiment to experience capitalism
Sampson, T. D. (2018) · AI & Society · doi:10.1007/s00146-018-0822-z · Cited by 8
Stability study of the optimal channel selection for emotion classification from EEG
Peng, H., Wang, Y., Chao, J., Huo, X., & Majoe, D. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217973 · Cited by 8
Emotion recognition in Arabic speech
Klaylat, S., Hamandi, L., Osman, Z. A., & Zantout, R. (2017) · Sensors Networks Smart and Emerging Technologies (SENSET) · doi:10.1109/senset.2017.8125028 · Cited by 9
Language, emotion, and the emotions: A computational introduction
Santos, D., & Maia, B. (2018) · Language and Linguistics Compass · doi:10.1111/lnc3.12279 · Cited by 8
Ross, P. R., & Tomico, O. (2009) · Cited by 8
Samsonovich, A. V. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-01328-8_3 · Cited by 8
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Kappas, A., Stower, R. E., & Vanman, E. J. (2020) · Springer eBooks · doi:10.1007/978-3-030-34964-6_8 · Cited by 9
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Jiang, D., Jin, D., Zhuang, J., Tan, D., Chen, D., & Liang, Y. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6269 · Cited by 8
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Aarts, E., Korst, J., & Verhaegh, W. (2004) · Philips Research · doi:10.1007/978-94-017-0703-9_1 · Cited by 8
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Bissinger, B., Märtin, C., & Michael Fellmann, M. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05409-9_25 · Cited by 8
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SHIIZUKA, H. (2006) · Journal of Japan Society of Kansei Engineering · doi:10.5057/kansei.6.4_3 · Cited by 8
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Haig, A. J., & Whitfield, A. (2001) · Swinburne figshare (Swinburne University of Technology) · doi:10.25916/sut.26273542 · Cited by 8
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Scherer, S. J., Oubbati, M., Schwenker, F., & Palm, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69369-7_22 · Cited by 8
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Lee, M., Cho, S., & Choi, E. (2007) · International Conference on Intelligent Pervasive Computing · doi:10.1109/ipc.2007.61 · Cited by 8
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Rani, P., & Sarkar, N. (2007) · International Journal of Advanced Robotic Systems · doi:10.5772/5716 · Cited by 8
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Riener, A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801851-4.00019-7 · Cited by 8
Representing Dispositions and Emotions in Simulated Combat
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Mizoguchi, H., Kanamori, T., Kagami, S., Hirao, K., Tanaka, M., Shigehara, T., & Mishima, T. (2004) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2002.1175638 · Cited by 8
Emotional Dialogs with an Embodied Agent
Cavalluzzi, A., Carolis, B. N. D., Carofiglio, V., & Grassano, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44963-9_13 · Cited by 8
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Andrade, F., Neves, J., Nováis, P., & Machado, J. M. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/1-4020-8139-1_14 · Cited by 8
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Macaulay, M., & Edmonds, E. (2004) · Journal of Educational Computing Research · doi:10.2190/uvlg-bkx0-j9ga-j3h4 · Cited by 8
Wilson, G. M., & Sasse, M. A. (2000) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/633292.633360 · Cited by 8
An extensible platform for the development of synchronous groupware
Licea, G., & Favela, J. (2000) · Information and Software Technology · doi:10.1016/s0950-5849(99)00098-1 · Cited by 8
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Ioannou, S. V., Wallace, M., Karpouzis, K. C., Raouzaiou, A. T., & Kollias, S. (2005) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2005.1452394 · Cited by 8
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Kaiser, S., & Wehrle, T. (2001) · Cited by 8
Modeling affective responses in intelligent tutoring systems
Perez, Y. H., Gamboa, R. M., & Ibarra, O. M. (2004) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2004.1357644 · Cited by 8
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Rzepka, R., Higuchi, S., Ptaszynski, M. E., & Araki, K. (2008) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2008.4811614 · Cited by 8
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Antunes, L., & Coelho, H. (2000) · Lecture notes in computer science · doi:10.1007/10720296_9 · Cited by 8
Effects of Task Demand and Time-on-Task on Psychophysiological Candidates for Biocybernetic Control
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A Computational Study on Emotions and Temperament in Multi-Agent SystemsPreprint
Reis, L. P., Barteneva, D., & Lau, N. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0809.4784 · Cited by 8
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Neto, R. D. F. B. (2006) · doi:10.11606/t.55.2006.tde-01032007-163908 · Cited by 8
Panksepp, J. (2000) · Neuropsychoanalysis · doi:10.1080/15294145.2000.10773278 · Cited by 8
Svikhnushina, E., & Pu, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13883 · Cited by 16
Kawamura, K., & Browne, W. N. (2009) · Encyclopedia of Complexity and Systems Science · doi:10.1007/978-0-387-30440-3_74 · Cited by 8
Kulkarni, S. (2022) · Wiley eBooks · doi:10.1002/9781119792376.ch6 · Cited by 8
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Vidyarthi, J. (2012) · Summit (Simon Fraser University) · Cited by 8
Speaking Style Authentication Using Suprasegmental Hidden Markov ModelsPreprint
Shahin, I. M. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09736 · Cited by 8
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Af-CAN: Multimodal Emotion Recognition Method Based on Situational Attention Mechanism
Zhang, X., Wang, M., Zeng, X., & Zhuang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3471613 · Cited by 7
Qian, C., Marques, J. A. L., & de Alexandria, A. R. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025328 · Cited by 12
Yusuf, H., Money, A., & Daylamani-Zad, D. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13755-7 · Cited by 14
Salles, J. (2025) · New Media & Society · doi:10.1177/14614448251385086 · Cited by 7
Isaee, H., & Barjesteh, H. (2026) · Human Arenas · doi:10.1007/s42087-026-00602-5 · Cited by 14
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Fu, E. Y., Leong, H. V., Ngai, G., Huang, M. X., & Chan, S. C. F. (2014) · IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW) · doi:10.1109/compsacw.2014.46 · Cited by 7
EEG-based emotion recognition in Chinese emotional words
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Ji, S.-Y., Jeong, B. K., & Jeong, D. H. (2019) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2019100103 · Cited by 7
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Carrillo, J. A. O., López, L. M. R., Esparrell, J. A. F., & Maldonado, Á. O. (2020) · Educatio Siglo XXI · doi:10.6018/educatio.432421 · Cited by 7
Zweigle, T. M. (2021) · Springer eBooks · doi:10.1007/978-3-658-31519-1_11 · Cited by 7
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Gmytrasiewicz, P. J., & Lisetti, C. (2000) · Cited by 7
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Udeh, C. P., Chen, L., Du, S., Li, M., & Wu, M. (2023) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2023.p0710 · Cited by 8
Joshi, R., & Jadeja, M. (2024) · The Springer series in applied machine learning · doi:10.1007/978-3-031-63821-3_2 · Cited by 9
Song, I., & Guido Governatori, A. (2006) · Lecture notes in computer science · doi:10.1007/978-3-540-36668-3_10 · Cited by 7
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Siegert, I., Böck, R., & Wendemuth, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34584-5_23 · Cited by 7
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Kang, H.-B. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-24594-0_23 · Cited by 7
User brain-driven evaluation of an educational 3D virtual environment
Carofiglio, V., Ricci, G., & Abbattista, F. (2015) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.1109/cisti.2015.7170553 · Cited by 7
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A Neuroscientific View on the Role of Emotions in Behaving Cognitive Agents
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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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