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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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de Gemmis, M., Lops, P., & Polignano, M. (2018) · Springer eBooks · doi:10.1007/978-1-4939-7131-2_110158 · Cited by 3
Essential principles for autonomous robotics
(2014) · Choice Reviews Online · doi:10.5860/choice.51-3861 · Cited by 3
Confronting Structural Inequities in AI for Education.Preprint
Madaio, M., Blodgett, S. L., Mayfield, E., & Dixon‐Román, E. J. (2021) · arXiv (Cornell University) · Cited by 3
Cross Domain Emotion Recognition using Few Shot Knowledge TransferPreprint
Olah, J., Baruah, S., Bose, D., & Narayanan, S. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.05021 · Cited by 4
Hairston, W. D., Chen, J., Barnes, M., Martinez, I., LaFiandra, M. E., Binseel, M. S., Scharine, A. A., Ericson, M. A., Vaughan, B., Lance, B. J., et al. (2012) · doi:10.21236/ada562494 · Cited by 3
Gesture, Posture, Facial Interfaces
Lee, D. (2020) · Springer eBooks · doi:10.1007/978-3-642-41610-1_25-1 · Cited by 3
The Ethical Implications of Artificial Intelligence on the Human SocietyPreprint
Ansari, W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4512301 · Cited by 3
Understanding Changes in Mental Workload during Task Execution
Iqbal, S. T., Adamczyk, P. D., Zheng, X. S., & Bailey, B. P. (2004) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 3
Hasson, C. (2011) · theses.fr (ABES) · doi:10.70675/8147f57dz1bcaz471dz9168ze3c68517a04a · Cited by 3
Robotic Faces: Exploring Dynamical Patterns of Social Interaction between Humans and Robots
Bennett, C. C. (2015) · IUScholarWorks (Indiana University) · Cited by 3
Moussaoui, K., & Farah, M. (2024) · 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP) · doi:10.1109/atsip62566.2024.10638977 · Cited by 2
The Application of Artificial Emotions in Artificial Intelligence
Gu, Y. (2022) · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · doi:10.2991/assehr.k.220105.090 · Cited by 2
zamani, M., Kheirollahi, M., Abad, M. J. A. E., rezaee, H., & Vafaee, F. (2022) · The Neuroscience Journal of Shefaye Khatam · doi:10.52547/shefa.10.3.68 · Cited by 2
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EmoCNN: Unleashing Human Emotions with Customized CNN Using Different Optimizers
Sahana, M., Umesh, P., Kodipalli, A., & Rao, T. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.124 · Cited by 2
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Ohara, R., Matsubara, F., & Narumi, T. (2025) · ACM Transactions on Applied Perception · doi:10.1145/3748823 · Cited by 2
Digital morphogenesis and bioAffective data: A computational framework for multisensory design
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Asseri, B., Abaker, E., Mogren, M. A., Alhefdhi, T., & Al‐Wabil, A. (2025) · AI · doi:10.3390/ai6090211 · Cited by 2
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Romeo, V. M. (2025) · Bioengineering · doi:10.3390/bioengineering12111257 · Cited by 2
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Macasawang, J. M. (2025) · Advances in Psychological Sciences and Applications · doi:10.56741/apsa.v1i03.1297 · Cited by 5
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Shelke, A., Kumar, A., Yadav, M., & Aseri, V. (2026) · Information systems engineering and management · doi:10.1007/978-3-032-14778-3_11 · Cited by 3
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Rehman, A., Nitin, V., & Uno, T. (2008) · doi:10.5772/6170 · Cited by 2
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Gorga, D., & Schneider, D. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-354-8.ch021 · Cited by 2
Pérez, J., Cerezo, E., & Arbeloa, F. J. S. (2016) · doi:10.1145/3005338.3005340 · Cited by 2
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隆, 城., & Swan, M. (2015) · Institutional Repositories DataBase (IRDB) · Cited by 2
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Wahl, M., Krüger, J., & Frommer, J. (2015) · Cited by 2
Fritsch, J., & Jacobsen, M. (2017) · ACM Conference Companion Publication on Designing Interactive Systems (DIS Companion) · doi:10.1145/3064857.3079158 · Cited by 2
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Aarts, E., & Wichert, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27780-4_1 · Cited by 2
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Song, K.-S., & Kwon, D. (2019) · IEEE RO-MAN · doi:10.1109/ro-man46459.2019.8956436 · Cited by 2
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Tallon, A. (2009) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780195170214.003.0007 · Cited by 2
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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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