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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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Sengers, P., Liesendahi, R., Magar, W., Seibert, C., Müller, B., Joachims, T., Geng, W., Mårtensson, P., & Höök, K. (2002) · Designing Interactive Systems Conference · doi:10.1145/778712.778728 · Cited by 51
van Oosterhout, A., Bruns, M., & Pyykko, S. J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174229 · Cited by 50
Speech emotion recognition based on multi‐feature and multi‐lingual fusion
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Emotion Cause Events: Corpus Construction and Analysis
Lee, S. Y. M., Chen, Y., Li, S., & Huang, C. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/38f43283cddk · Cited by 50
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D’Mello, S. K., Graesser, A. C., & King, B. G. (2010) · Human-Computer Interaction · doi:10.1080/07370024.2010.499850 · Cited by 50
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Measurement of distal EMG signals using a wearable device for reading facial expressions
Gruebler, A., & Suzuki, K. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626504 · Cited by 50
Eye gaze patterns in emotional pictures
Lanatà, A., Valenza, G., & Scilingo, E. P. (2012) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-012-0147-6 · Cited by 50
Advanced Information Retrieval
Ren, F., & Bracewell, D. B. (2009) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2008.12.082 · Cited by 50
A model of emotions for situated agents
Parunak, H. V. D., Bisson, R., Brueckner, S., Matthews, R., & Sauter, J. (2006) · Adaptive Agents and Multi-Agents Systems · doi:10.1145/1160633.1160810 · Cited by 50
CHI and the future robot enslavement of humankind
Kirman, B. J., Linehan, C., Lawson, S. W., & O'Hara, D. (2013) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2468356.2468740 · Cited by 50
A tetradic analysis of GIS and society using McLuhan's law of the media
Sui, D. Z., & Goodchild, M. F. (2003) · Canadian Geographies / Géographies canadiennes · doi:10.1111/1541-0064.02e08 · Cited by 50
Hierarchical affective content analysis in arousal and valence dimensions
Xu, M. G., Xu, C., He, X., Jin, J. S., Luo, S., & Rui, Y. (2012) · Signal Processing · doi:10.1016/j.sigpro.2012.06.026 · Cited by 50
Eye tracking and pupil size variation as response to affective stimuli: a preliminary study
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Deep learning and SVM‐based emotion recognition from Chinese speech for smart affective services
Zhang, W., Zhao, D., Chai, Z., Yang, L. T., Liu, X., Gong, F., & Yang, S. (2017) · Software Practice and Experience · doi:10.1002/spe.2487 · Cited by 50
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Wechsung, I., & Moor, K. D. (2014) · T-labs series in telecommunication services · doi:10.1007/978-3-319-02681-7_3 · Cited by 50
Architecture-Based Conceptions of Mind
Sloman, A. (2002) · Springer eBooks · doi:10.1007/978-94-017-0475-5_3 · Cited by 50
Understanding affect in design: an outline conceptual framework
Aboulafia, A., & Bannon, L. J. (2004) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922031000086708 · Cited by 50
Sloman, A., & Chrisley, R. (2004) · Cognitive Systems Research · doi:10.1016/j.cogsys.2004.06.004 · Cited by 50
Long term activity monitoring with a wearable sensor node
Laerhoven, K. V., Gellersen, H. W., & Malliaris, Y. (2006) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2006.39 · Cited by 50
Zeng, Z., Hu, Y., Liu, M., Fu, Y., & Huang, T. S. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180661 · Cited by 49
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Xu, K., & Liao, T. (2019) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmz023 · Cited by 50
Yannakakis, G. N., Karpouzis, K. C., Paiva, A., & Hudlická, E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_62 · Cited by 50
Negative Affect Reduces Team Awareness
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Liu, Y.-H., Wu, C., Cheng, W.-T., Hsiao, Y.-T., Chen, P.-M., & Teng, J. (2014) · Sensors · doi:10.3390/s140813361 · Cited by 48
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Chen, J., Luo, N., Liu, Y., Liu, L., Zhang, K., & Kołodziej, J. (2014) · Computing · doi:10.1007/s00607-014-0430-9 · Cited by 49
Robust emotion recognition in noisy speech via sparse representation
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Segment-based emotion recognition from continuous Mandarin Chinese speech
Yeh, J.-H., Pao, T.-L., Lin, C., Tsai, Y.-W., & Chen, Y.-T. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.10.027 · Cited by 49
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Zhang, F., Hu, M., Che, W., Lin, H. Z., & Fang, C. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7010036 · Cited by 49
Rashmi, M., Ashwin, T. S., & Guddeti, R. M. R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09741-5 · Cited by 49
Moschona, D. S. (2020) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia49877.2020.9277291 · Cited by 49
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Picard, R. W., Wexelblat, A., & Nass, C. (2002) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/506443.506552 · Cited by 49
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Wong, H., & Ip, H. H. S. (2008) · Encyclopedia of Multimedia · doi:10.1007/978-0-387-78414-4_339 · Cited by 48
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Ferreira, P., Sanches, P., Höök, K., & Jaensson, T. (2008) · Nordic Conference on Human-Computer Interaction · doi:10.1145/1463160.1463174 · Cited by 47
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Li, L. P., Jurić, B., & Brodie, R. J. (2018) · Journal of service management · doi:10.1108/josm-08-2016-0235 · Cited by 55
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Schlegel, K., Sommer, N. R., & Mortillaro, M. (2025) · Communications Psychology · doi:10.1038/s44271-025-00258-x · Cited by 46
Emotion and Motivation in Cognitive Assistive Technologies for Dementia
Robillard, J. M., & Hoey, J. (2018) · Computer · doi:10.1109/mc.2018.1731059 · Cited by 46
Cano, S. P., González, C. S. G., Gil, R., & Albiol-Pérez, S. (2021) · Sensors · doi:10.3390/s21155166 · Cited by 46
Machine learning in biosignals processing for mental health: A narrative review
Sajno, E., Bartolotta, S., Tuena, C., Cipresso, P., Pedroli, E., & Riva, G. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1066317 · Cited by 59
Villegas-Ch, W. E., García-Ortiz, J., & Sánchez-Viteri, S. (2023) · IEEE Access · doi:10.1109/access.2023.3267007 · Cited by 46
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Sheng, Q., Zhou, Z., Li, J., Mi, X., Xiang, P., Chen, Z., Xu, H., Jia, S., Wu, X., Cui, Y., et al. (2025) · SmartBot · doi:10.1002/smb2.12008 · Cited by 57
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Shaker, N., Asteriadis, S., Yannakakis, G. N., & Karpouzis, K. C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_68 · Cited by 46
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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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