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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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Alsuradi, H., & Eid, M. A. (2023) · IEEE Transactions on Haptics · doi:10.1109/toh.2023.3270666 · Cited by 7
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Sharma, S., Ghorpade, S., Sahni, A., & Saluja, N. (2014) · Cited by 7
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Xie, G., Liu, G., & Zhang, S. (2016) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2016.7554917 · Cited by 7
Benţa, K. (2006) · IEEE International Conference on Wireless, Mobile and Ubiquitous Technology in Education (WMUTE) · doi:10.1109/wmte.2005.8 · Cited by 7
Chalup, S. K., & Ostwald, M. J. (2010) · NOVA (University of Newcastle Australia) · Cited by 7
Ren, P., Barreto, A. B., Ma, X., Liu, S., Zhang, M., Wang, Y., Dong, Y., & Yao, D. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2946829 · Cited by 7
Dou, J. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1922-2_35 · Cited by 7
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Horvat, M. (2020) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro48935.2020.9245268 · Cited by 7
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Farahi, B. (2018) · ACADIA quarterly · doi:10.52842/conf.acadia.2018.206 · Cited by 7
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Huang, L., Xin, L., Zhao, L., & Tao, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_71 · Cited by 7
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Vanhala, T., & Surakka, V. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_25 · Cited by 7
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Snibbe, S. S., Scheeff, M., & Rahardja, K. (1999) · Cited by 7
Feature Selection in Audiovisual Emotion Recognition Based on Rough Set Theory
Yang, Y., Wang, G., Chen, P., Zhou, J., & He, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-71663-1_18 · Cited by 7
Guess Who? An Interactive and Entertaining Game-Like Platform for Investigating Human Emotions
Ahmad, M., Tariq, H., Saeed, M., Shahid, S., & Krahmer, E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21616-9_61 · Cited by 7
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Lemmens, P. M. C. (2005) · SMARTech Repository (Georgia Institute of Technology) · Cited by 7
Indirectly Visible Bayesian Student Models.
Zapata‐Rivera, D. (2007) · Cited by 7
An Emotion Recognition System Based on Rough Set Theory
Yang, Y., Wang, G., & Chen, P. (2006) · Active Media Technology · Cited by 7
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Affect-Sensitive Multi-Modal Monitoring in Ubiquitous Computing: Advances and Challenges.
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Lee, B. P.-H., Kao, E. C.-C., & Soo, V. (2006) · Lecture notes in computer science · doi:10.1007/11821830_27 · Cited by 7
Can Machines Adequately Simulate Human Emotion?
Spackman, M. P. (2004) · Theory & Psychology · doi:10.1177/0959354304048105 · Cited by 8
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Shahid, S., Krahmer, E., Swerts, M. G. J., Melder, W. A., & Neerincx, M. A. (2009) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1520340.1520562 · Cited by 7
Pentland, A. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101302 · Cited by 7
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Roda, C., & Nabeth, T. (2005) · Cited by 7
Plot-polling: Collaborative Knowledge Visualization for Online Discussions
Ivanov, A., Erickson, T. D., & Cyr, D. (2006) · International Conference on Information Visualisation (IV) · doi:10.1109/iv.2006.84 · Cited by 7
HMM recognition of expressions in unrestrained video intervals
Landabaso, J. L., Pardis, M., & Bonafonte, A. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2003.1199141 · Cited by 7
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Zhang, S., & Hui, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.1648 · Cited by 8
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Neviarouskaya, A. (2011) · Institutional Repositories DataBase (IRDB) · doi:10.15083/00004103 · Cited by 7
Castellano, G., Karpouzis, K. C., Peters, C., & Martin, J. (2009) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0031-7 · Cited by 7
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Madan, A. (2005) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7
Aman, S. (2007) · uO Research (University of Ottawa) · doi:10.20381/ruor-18743 · Cited by 7
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Momtaz, P. P. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3305765 · Cited by 7
Self motivated planning in autonomous agents.
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Raptis, S. (2013) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2013.6719197 · Cited by 6
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Barman, A., & Dutta, P. (2017) · IEEE Calcutta Conference (CALCON) · doi:10.1109/calcon.2017.8280685 · Cited by 6
Yuksel, B. F., Oleson, K. B., Chang, R., & Jacob, R. J. K. (2019) · Springer series on cultural computing · doi:10.1007/978-3-319-92069-6_11 · Cited by 6
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Sousa, J., Santos, S., André, L., & Ferreira, J. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60875-9_8 · Cited by 6
Kumar, J. A. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105150 · Cited by 9
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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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