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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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Jou, B., Bhattacharya, S., & Chang, S. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2656408 · Cited by 81
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Polignano, M., Narducci, F., de Gemmis, M., & Semeraro, G. M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114382 · Cited by 81
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
du Boulay, B., Avramides, K., Luckin, R., Martínez-Mirón, E. A., Rebolledo‐Mendez, G., & Carr, A. (2010) · International Journal of Artificial Intelligence in Education · doi:10.3233/jai-2010-0007 · Cited by 83
Facial animation parameters extraction and expression recognition using Hidden Markov Models
Pardàs, M., & Bonafonte, A. (2002) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(02)00078-4 · Cited by 81
Ren, F., & Quan, C. (2012) · Inf. Technol. Manag. · doi:10.1007/s10799-012-0138-5 · Cited by 92
Remarks on SVM-based emotion recognition from multi-modal bio-potential signals
Takahashi, K. (2005) · IEEE RO-MAN · doi:10.1109/roman.2004.1374736 · Cited by 81
Affective Information Processing and Recognizing Human Emotion
Ren, F. (2009) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2008.12.065 · Cited by 81
Emotions in the practice of psychotherapy: Clinical implications of affect theories.
Plutchik, R. (2000) · American Psychological Association eBooks · doi:10.1037/10366-000 · Cited by 81
EEG-based emotion recognition using Recurrence Plot analysis and K nearest neighbor classifier
Bahari, F., & Janghorbani, A. (2013) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2013.6782224 · Cited by 80
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Tsiakas, K., Abujelala, M., & Makedon, F. S. (2018) · Technologies · doi:10.3390/technologies6020049 · Cited by 80
The Potential of Game-Based Digital Biomarkers for Modeling Mental Health
Mandryk, R. L., & Birk, M. V. (2019) · JMIR Mental Health · doi:10.2196/13485 · Cited by 93
Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis
Gao, Y., Wang, X., Potter, T. B. H., Zhang, J., & Zhang, Y. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108904 · Cited by 80
Field trial of networked social robots in a shopping mall
Shiomi, M., Kanda, T., Glas, D. F., Satake, S., Ishiguro, H., & Hagita, N. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354242 · Cited by 80
Zhao, X., & Zhang, S. (2012) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/1687-6180-2012-20 · Cited by 81
Adaptation in Affective Video Games: A Literature Review
Bontchev, B. (2016) · Cybernetics and Information Technologies · doi:10.1515/cait-2016-0032 · Cited by 82
Coverage of Emotion Recognition for Common Wearable Biosensors
Hui, T., & Sherratt, R. S. (2018) · Biosensors · doi:10.3390/bios8020030 · Cited by 82
Hoey, J., Schröder, T., & Alhothali, A. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2015.09.004 · Cited by 83
Djajadiningrat, J. P., Overbeeke, C. J., & Wensveen, S. S. A. G. (2000) · Designing Augmented Reality Environments (DARE) · doi:10.1145/354666.354680 · Cited by 80
Multi-layer affective computing model based on emotional psychology
Zhou, Q. (2017) · Electronic Commerce Research · doi:10.1007/s10660-017-9265-8 · Cited by 79
Consumer Grade Brain Sensing for Emotion Recognition
Lakhan, P., Banluesombatkul, N., Changniam, V., Dhithijaiyratn, R., Leelaarporn, P., Boonchieng, E., Hompoonsup, S., & Wilaiprasitporn, T. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2928781 · Cited by 79
Context-Aware Affective Graph Reasoning for Emotion Recognition
Zhang, M., Liang, Y., & Ma, H. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00034 · Cited by 79
How To Build a Better Robot . . . for Quick-Service Restaurants
Zemke, D., Tang, J., Raab, C., & Kim, J. (2020) · Journal of Hospitality & Tourism Research · doi:10.1177/1096348020946383 · Cited by 75
Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition
Wang, Z., Wang, Y., Zhang, J., Hu, C., Yin, Z., & Song, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3165280 · Cited by 80
Human Behavior Analysis for Highlight Ranking in Broadcast Racket Sports Video
Zhu, G., Huang, Q., Xu, C., Xing, L., Gao, W., & Yao, H. (2007) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2007.902847 · Cited by 79
Tung, F.-W., & Deng, Y.-S. (2006) · Interactive Learning Environments · doi:10.1080/10494820600924750 · Cited by 79
Situated play in a tangible interface and adaptive audio museum guide
Wakkary, R., & Hatala, M. (2006) · Personal and Ubiquitous Computing · doi:10.1007/s00779-006-0101-8 · Cited by 79
Prediction of the Distribution of Perceived Music Emotions Using Discrete Samples
Yang, Y., & Chen, H. H. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2118752 · Cited by 78
Building cognitively rich agents using the SIM_Agent toolkit
Sloman, A., & Logan, B. S. (1999) · Communications of the ACM · doi:10.1145/295685.295704 · Cited by 79
Understanding Robot Acceptance
Beer, J. M., Prakash, A., Mitzner, T. L., & Rogers, W. A. (2011) · SMARTech Repository (Georgia Institute of Technology) · Cited by 78
Toolbox for Emotional feAture extraction from Physiological signals (TEAP)
Soleymani, M., Villaro-Dixon, F., Pun, T., & Chanel, G. (2017) · Frontiers in ICT · doi:10.3389/fict.2017.00001 · Cited by 79
Understanding the New Human Dynamics in Smart Spaces and Places: Toward a Splatial Framework
Shaw, S., & Sui, D. Z. (2019) · Annals of the American Association of Geographers · doi:10.1080/24694452.2019.1631145 · Cited by 78
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
Zhang, G., Davoodnia, V., & Etemad, A. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3210441 · Cited by 78
Measuring instant emotions based on facial expressions during computer-based assessment
Terzis, V., Moridis, C. N., & Economides, A. A. (2011) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0477-y · Cited by 78
Towards Adaptive Social Behavior Generation for Assistive Robots Using Reinforcement Learning
Hemminghaus, J., & Kopp, S. (2017) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2909824.3020217 · Cited by 78
Evoking Physiological Synchrony and Empathy Using Social VR With Biofeedback
Salminen, M., Järvelä, S., Ruonala, A., Harjunen, V. J., Hamari, J., Jacucci, G., & Ravaja, N. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2958657 · Cited by 83
Affective Visualization Design: Leveraging the Emotional Impact of Data
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Mayrhofer, W., Iellatchitch, A., Meyer, M., Steyrer, J. M., Schiffinger, M., & Strunk, G. (2004) · Journal of Management Development · doi:10.1108/02621710410558477 · Cited by 77
Promoting Interactions Between Humans and Robots Using Robotic Emotional Behavior
Ficocelli, M., Terao, J., & Nejat, G. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2492999 · Cited by 77
Facial Expression Recognition in Daily Life by Embedded Photo Reflective Sensors on Smart Eyewear
Masai, K., Sugiura, Y., Ogata, M., Kunze, K. S., İnami, M., & Sugimoto, M. (2016) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2856767.2856770 · Cited by 82
Pijeira‐Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883897 · Cited by 81
Shute, V. J., & Zapata‐Rivera, D. (2007) · ETS Research Report Series · doi:10.1002/j.2333-8504.2007.tb02047.x · Cited by 81
Affect prediction from physiological measures via visual stimuli
Zhou, F., Qu, X., Helander, M. G., & Jiao, J. R. (2011) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2011.07.005 · Cited by 76
Adapting Software with Affective Computing: A Systematic Review
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Han, S., Kelly, E. W., Nikou, S., & Svee, E.-O. (2021) · AI & Society · doi:10.1007/s00146-021-01247-4 · Cited by 93
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Matthias, A. (2015) · Kennedy Institute of Ethics journal · doi:10.1353/ken.2015.0007 · Cited by 76
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Frey, J., Mühl, C., Lotte, F., & Hachet, M. (2014) · International Conference on Physiological Computing Systems (PhyCS) · doi:10.5220/0004708102140223 · Cited by 76
Khan, M. M., Ingleby, M., & Ward, R. D. (2006) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/1152934.1152939 · Cited by 79
Sengers, P., Boehner, K., Mateas, M., & Gay, G. K. (2007) · Personal and Ubiquitous Computing · doi:10.1007/s00779-007-0161-4 · Cited by 79
Does Interactive Media Enhance the Management of Stress? Suggestions from a Controlled Study
Villani, D., & Riva, G. (2011) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2011.0141 · Cited by 80
Wilhelm, F. H., Pfaltz, M. C., & Grossman, P. (2005) · Interacting with Computers · doi:10.1016/j.intcom.2005.07.001 · Cited by 75
Sloman, A. (2000) · Advances in consciousness research · doi:10.1075/aicr.19.10slo · Cited by 76
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Gervais, R., Frey, J., Gay, A., Lotte, F., & Hachet, M. (2016) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/2839462.2839486 · Cited by 75
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Ng, G. W., & Leung, W. C. (2020) · Journal of Artificial Intelligence and Consciousness · doi:10.1142/s2705078520300042 · Cited by 75
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Lin, Y., Leng, H., Yang, G., & Cai, H. (2007) · IEEE Sensors Journal · doi:10.1109/jsen.2007.894923 · Cited by 77
Craig, S. D., Driscoll, D. M., & Gholson, B. (2004) · Journal of educational multimedia and hypermedia · Cited by 75
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Anselme, P., & Robinson, M. J. F. (2015) · Journal of Experimental Psychology Animal Learning and Cognition · doi:10.1037/xan0000090 · Cited by 82
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Voss, C., Washington, P. Y., Haber, N., Kline, A., Daniels, J., Fazel, A., De, T., McCarthy, B., Feinstein, C., Winograd, T. A., et al. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968310 · Cited by 73
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Andjelkovic, I., Parra, D., & O’Donovan, J. (2016) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/2930238.2930280 · Cited by 76
Han, J., Zhang, Z., Cummins, N., & Schuller, B. W. (2019) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2019.2901088 · Cited by 86
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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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