The DOI matches its Crossref record.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cited by
Sorted by citations
Items 1,601–1,700 of 7,380
THE ROLE OF EMOTION IN PROBLEM SOLVING
Belavkin, R. V. (2001) · Cited by 36
Essa, I. A. (1999) · doi:10.1609/aimag.v20i2.1457 · Cited by 36
Preface to the special issue on personalization and behavior change
Masthoff, J., Grasso, F., & Ham, J. R. C. (2014) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-014-9151-1 · Cited by 36
Towards a Technology of Nonverbal Communication
Vinciarelli, A., & Mohammadi, G. (2011) · IGI Global eBooks · doi:10.4018/978-1-61692-892-6.ch007 · Cited by 36
Risk and prosocial behavioural cues elicit human-like response patterns from AI chatbots
Zhao, Y., Xu, L., Huang, Z., Peng, K., Seligman, M. E. P., Li, E., & Yu, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-55949-y · Cited by 39
Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2010.02.003 · Cited by 35
Exploring differences in how men and women respond to threats to positive face on social media
Chen, G. M., & Abedin, Z. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.05.029 · Cited by 35
Magagnin, V., Mauri, M., Cipresso, P., Mainardi, L. T., Brown, E., Cerutti, S., Villamira, M. A., & Barbieri, R. (2010) · PubMed · Cited by 35
Recognizing, Modeling, and Responding to Users’ Affective States
Prendinger, H., Mori, J., & Ishizuka, M. (2005) · Lecture notes in computer science · doi:10.1007/11527886_9 · Cited by 35
Pupil Variation Applied to the Eye Tracking Control of an Endoscopic Manipulator
Cao, Y., Miura, S., Kobayashi, Y., Kawamura, K., Sugano, S., & Fujie, M. G. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2521894 · Cited by 35
Using affective state to adapt characters, NPCs, and the environment in a first-person shooter game
Negini, F., Mandryk, R. L., & Stanley, K. G. (2014) · IEEE Games, Entertainment, Media Conference (GEM) · doi:10.1109/gem.2014.7048094 · Cited by 37
Driver frustration detection from audio and video in the wild
Abdić, I., Fridman, L., McDuff, D., Marchi, E., Reimer, B., & Schuller, B. W. (2016) · OPUS (Augsburg University) · Cited by 35
Theories and Models of Teams and Groups
Reiter‐Palmon, R., Sinha, T., Gevers, J., Odobez, J., & Volpe, G. (2017) · Small Group Research · doi:10.1177/1046496417722841 · Cited by 41
AMHUSE: a multimodal dataset for HUmour SEnsing
Boccignone, G., Conte, D., Cuculo, V., & Lanzarotti, R. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136806 · Cited by 35
Do Judges Need to Be Human? The Implications of Technology for Responsive Judging
Sourdin, T., & Cornes, R. (2018) · Ius gentium · doi:10.1007/978-981-13-1023-2_4 · Cited by 37
Human-exhibition interaction (HEI) in designing exhibitions: A systematic literature review
Wang, N., & Xia, L. (2018) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2018.07.009 · Cited by 35
Multistage classification scheme to enhance speech emotion recognition
Poorna, S. S., & Nair, G. J. (2019) · International Journal of Speech Technology · doi:10.1007/s10772-019-09605-w · Cited by 35
Rethinking Liveness in the Digital Age
Sanden, P. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316676639.017 · Cited by 36
An Emotion Recognition Method for Game Evaluation Based on Electroencephalogram
Du, G., Zhou, W., Li, C., Li, D., & Liu, P. X. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3023966 · Cited by 35
Speaker Recognition in Emotional Environment
Koolagudi, S. G., Sharma, K. S., & Rao, K. S. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-32112-2_15 · Cited by 35
Emotional awareness in collaborative systems
García, O. P., Favela, J., & Machorro, R. (2003) · String Processing and Information Retrieval · doi:10.1109/spire.1999.796607 · Cited by 35
'Affective' computing and emotion recognition systems
Bullington, J. C. (2005) · Annual Conference on Information Security Curriculum Development (InfoSecCD) · doi:10.1145/1107622.1107644 · Cited by 35
Emotive qualities in lip-synchronized robot speech
Breazeal, C. (2003) · Advanced Robotics · doi:10.1163/156855303321165079 · Cited by 35
Fundamental Issues in Affective Intelligent Social Machines
Duffy, B. R. (2008) · The Open Artificial Intelligence Journal · doi:10.2174/1874061800802010021 · Cited by 35
Towards estimating computer users’ mood from interaction behaviour with keyboard and mouse
Khan, I. A., Brinkman, W., & Hierons, R. M. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-2331-z · Cited by 35
Animating an interactive conversational character for an educational game system
Corradini, A., Mehta, M., Bernsen, N. O., & Charfuelàn, M. (2005) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1040830.1040872 · Cited by 35
Mikhail, M., El-Ayat, K., Coan, J. A., & Allen, J. J. B. (2012) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2013.050696 · Cited by 35
The role of trust and deception in virtual societies
Castelfranchi, C., & Tan, Y. (2005) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2001.927042 · Cited by 35
Features for multimodal emotion recognition: An extensive study
Paleari, M., Chellali, R., & Huet, B. (2010) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2010.5518574 · Cited by 35
An Emotional Model for a Guide Robot
Alvarez, M., Galán, R., Matı́a, F., Rodríguez-Losada, D., & Jiménez, A. (2010) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2010.2046734 · Cited by 35
Squeeze, rock, and roll; can tangible interaction with affective products support stress reduction?
Bruns, M., Keyson, D. V., & Hummels, C. (2008) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/1347390.1347413 · Cited by 35
Hirshfield, L., Bobko, P., Barelka, A. J., Hirshfield, S. H., Farrington, M. T., Gulbronson, S., & Paverman, D. (2014) · Advances in Human-Computer Interaction · doi:10.1155/2014/101038 · Cited by 35
Speech Emotion Recognition Using Support Vector Machines
Yu, C., Tian, Q., Cheng, F. X., & Zhang, S. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-21402-8_35 · Cited by 35
Using automated facial expression analysis for emotion and behavior prediction
Ahn, S. J., Bailenson, J. N., Fox, J., & Jabon, M. E. (2015) · Routledge eBooks · doi:10.4324/9780203885390.ch20 · Cited by 35
Consciousness, emotion, and imagination a brain-inspired architecture for cognitive robotics
Goldie, P., & Shanahan, M. (2005) · Research Explorer (The University of Manchester) · Cited by 35
Recognising affect in text using pointwise-mutual information
Read, J. (2010) · Cited by 35
Robots that can hear, understand and talk
Prasad, R., Saruwatari, H., & Shikano, K. (2004) · Advanced Robotics · doi:10.1163/156855304774195064 · Cited by 35
Shakun, M. F. (2001) · Group Decision and Negotiation · doi:10.1023/a:1008708222157 · Cited by 35
The Effects of Emotion of Voice in Synthesized and Recorded Speech
Nass, C., Foehr, U. G., Brave, S., & Somoza, M. (2001) · Cited by 35
Toward affective dialogue management using partially observable Markov decision processes
Bui, T. (2008) · doi:10.3990/1.9789036527149 · Cited by 34
Representation and recognition of action in interactive spaces
Bobick, A., & Pinhanez, C. S. (1999) · Cited by 35
The Ambiguous World of Emotion RepresentationPreprint
Sethu, V., Provost, E. M., Epps, J., Busso, C., Cummins, N., & Narayanan, S. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00360 · Cited by 49
van Lotringen, C. M., Lusi, B., Westerhof, G. J., Ludden, G., Kip, H., Kelders, S. M., & Noordzij, M. L. (2023) · JMIR Mental Health · doi:10.2196/42403 · Cited by 38
Selective Acoustic Feature Enhancement for Speech Emotion Recognition With Noisy Speech
Leem, S.-G., Fulford, D., Onnela, J., Gard, D. E., & Busso, C. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3340603 · Cited by 45
Real-Time Emotion Recognition from Speech Using Echo State Networks
Scherer, S. J., Oubbati, M., Schwenker, F., & Palm, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69939-2_20 · Cited by 34
A Formal Model of Emotions: Integrating Qualitative and Quantitative Aspects
Steunebrink, B. R., Dastani, M. M., & Ch., M. J.-J. (2008) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-58603-891-5-256 · Cited by 34
Memory Models for Intelligent Social Companions
Lim, M. Y. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-25691-2_10 · Cited by 34
Authenticity in the Age of Digital Companions
Turkle, S. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511978036.008 · Cited by 34
Using Paralinguistic Cues in Speech to Recognise Emotions in Older Car Drivers
Jones, C. M., & Jonsson, I.-M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85099-1_20 · Cited by 34
Intraindividual and interindividual multimodal emotion analyses in Human-Machine-Interaction
Böck, R., Glüge, S., Wendemuth, A., Limbrecht, K., Walter, S., Hrabal, D., & Traue, H. C. (2012) · IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support · doi:10.1109/cogsima.2012.6188409 · Cited by 34
Sonderegger, A., Sauer, J., & Eichenberger, J. (2013) · Behaviour and Information Technology · doi:10.1080/0144929x.2013.853835 · Cited by 34
Wang, Y., & Tseng, M. M. (2013) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2013.2248729 · Cited by 34
Content-Based Prediction of Movie Style, Aesthetics, and Affect: Data Set and Baseline Experiments
Tarvainen, J., Sjöberg, M., Westman, S., Laaksonen, J. T., & Oittinen, P. T. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2357688 · Cited by 34
Power to the People: Leveraging Human Physiological Traits to Control Microprocessor Frequency
Shye, A., Pan, Y., Scholbrock, B., Miller, J. S., Memik, G., Dinda, P. A., & Dick, R. P. (2009) · Cited by 34
Emotion in Human-Computer Interaction
Peter, C., & Urban, B. (2012) · Springer eBooks · doi:10.1007/978-1-4471-2804-5_14 · Cited by 35
Emotion Classification in Arabic Poetry using Machine Learning
Alsharif, O., Alshamaa, D., & Ghneim, N. (2013) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.19687323 · Cited by 34
The Computational Paralinguistics Challenge
Schuller, B. W. (2012) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 34
Towards detecting programmers’ stress on the basis of keystroke dynamics
Kołakowska, A. (2016) · Annals of Computer Science and Information Systems · doi:10.15439/2016f263 · Cited by 34
The life of faith as a work of art: a Rabbinic theology of faith
Lebens, S. (2016) · International Journal for Philosophy of Religion · doi:10.1007/s11153-016-9600-3 · Cited by 34
Pham, P., & Wang, J. (2017) · International Conference on Intelligent User Interfaces · doi:10.1145/3025171.3025186 · Cited by 31
Effects of Ambient Lighting Displays on Peripheral Activity Awareness
Davis, K., Owusu, E., Marcenaro, L., Feijs, L. M. G., Regazzoni, C. S., & Hu, J. (2017) · IEEE Access · doi:10.1109/access.2017.2703866 · Cited by 35
Clerico, A., Tiwari, A., Gupta, R., Jayaraman, S., & Falk, T. H. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00115 · Cited by 34
Intersession Instability in fNIRS-Based Emotion Recognition
Wang, F., Mao, M., Duan, L. F., Huang, Y., Li, Z., & Zhu, C. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2842464 · Cited by 35
Emotional ANN (EANN): A New Generation of Neural Networks for Hydrological Modeling in IoT
Nourani, V., Molajou, A., Najafi, H., & Mehr, A. D. (2019) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-04110-6_3 · Cited by 34
Hand Gesture Recognition for Human Computer Interaction and Its Applications in Virtual Reality
Gupta, S., Bagga, S., & Sharma, D. K. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-35252-3_5 · Cited by 34
Evolutionary Processes in Quantum Decision Theory
Yukalov, V. I. (2020) · Entropy · doi:10.3390/e22060681 · Cited by 32
Human–Robot Interactions and Affective Computing: The Ethical Implications
Devillers, L. Y. (2021) · Springer eBooks · doi:10.1007/978-3-030-54173-6_17 · Cited by 34
Luo, J., Wu, M., Wang, Z., Chen, Y., & Yang, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.064 · Cited by 34
Moctezuma, L. A., Abe, T., & Molinas, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-07517-5 · Cited by 39
Emotion recognition based on MPEG-4 Facial Animation Parameters
Pardàs, M., Bonafonte, A., & Landabaso, J. L. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5745440 · Cited by 34
The dynamic emotion recognition system based on functional connectivity of brain regions
Khosrowabadi, R., Heijnen, M., Wahab, A., & Quek, C. H. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548102 · Cited by 36
A novel approach to express emotions through a flock of virtual beings
Ibanez, J., Delgado-Mata, C., & Gomez-Caballero, F. (2007) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2007.39 · Cited by 34
van Allen, P., McVeigh-Schultz, J. R., Brown, B., Kim, H. M., & Lara, D. (2013) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2468356.2468746 · Cited by 34
Tsianos, N., Lekkas, Z., Germanakos, P., Mourlas, C., & Samaras, G. (2009) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2009.29 · Cited by 34
EEG-based emotion recognition using hybrid filtering and higher order crossings
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349513 · Cited by 34
Practical considerations on the use of preference learning for ranking emotional speech
Lotfian, R., & Busso, C. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472670 · Cited by 34
Emotions in Context-Aware Recommender Systems
Zheng, Y., Mobasher, B., & Burke, R. (2017) · Emotions and Personality in Personalized Services · doi:10.1007/978-3-319-31413-6_15 · Cited by 33
Zhu, X., & Chen, Z. (2019) · The Visual Computer · doi:10.1007/s00371-019-01660-3 · Cited by 35
Affective communication for implicit human-machine interaction
Rani, P., Sarkar, N., Smith, C. A., & Adams, J. A. (2004) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2003.1245758 · Cited by 34
One-Class Classification for Spontaneous Facial Expression Analysis
Zeng, Z., Fu, Y., Roisman, G. I., Wen, Z., Hu, Y., & Huang, T. S. (2006) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fgr.2006.83 · Cited by 34
Virtual reality exposure therapy for social phobia
Herbelin, B. (2005) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3351 · Cited by 34
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and PerspectivesPreprint
Han, J., Zhang, Z.-X., Cummins, N., & Schuller, B. (2018) · arXiv.org · doi:10.48550/arxiv.1809.08927 · Cited by 66
Intelligent autonomous agents and trust in virtual reality
Sun, N., & Botev, J. (2021) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2021.100146 · Cited by 36
The Cambridge Companion to Music in Digital Culture
Cook, N., Ingalls, M. M., & Trippett, D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316676639 · Cited by 38
Pabba, C., Bhardwaj, V., & Kumar, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16388-5 · Cited by 35
Emotionally adaptive support: a narrative review of affective computing for mental health
Schlicher, M., Li, Y., Murthy, S. M. K., Sun, Q., & Schuller, B. W. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1657031 · Cited by 46
Cid, F., Prado, J. A., Bustos, P., & López, P. J. N. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696662 · Cited by 33
Understanding the relationship between physiological signals and digital game-based learning outcome
Wu, C., Tzeng, Y.-L., & Huang, Y. M. (2014) · Journal of Computers in Education · doi:10.1007/s40692-014-0006-x · Cited by 35
Part-Based Annotation of Virtual 3D Shapes
Robbiano, F., Attene, M., Spagnuolo, M., & Falcidieno, B. (2007) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2007.8 · Cited by 33
Rising to the Challenge: An Emotion-Driven Approach Toward Adaptive Serious Games
Schrader, C., Brich, J., Frommel, J., Riemer, V., & Rogers, K. (2017) · Springer eBooks · doi:10.1007/978-3-319-51645-5_1 · Cited by 38
Wearable tech, bodies, and gender
Wissinger, E. (2017) · Sociology Compass · doi:10.1111/soc4.12514 · Cited by 34
Chen, Z., Schulz, S., Qiu, M., Yang, W. C., He, X., Yang, L., & Yang, L. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.09.003 · Cited by 32
Emotion Recognition by Facial Features using Recurrent Neural Networks
Mostafa, A., Khalil, M. I., & Abbas, H. M. (2018) · International Conference on Computer Engineering & Systems (ICCES) · doi:10.1109/icces.2018.8639182 · Cited by 33
Ethical Challenges in Digital Psychology and Cyberpsychology
Parsons, T. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108553384 · Cited by 33
Speaker-Invariant Adversarial Domain Adaptation for Emotion Recognition
Yin, Y., Huang, B., Wu, Y., & Soleymani, M. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418813 · Cited by 34
Straker, K., Wrigley, C., & Rosemann, M. (2015) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2015.06.004 · Cited by 33
Emotion Recognition Based on Multimodal Information
Zeng, Z., Pantić, M., & Huang, T. S. (2008) · Springer eBooks · doi:10.1007/978-1-84800-306-4_14 · Cited by 33
User Interfaces and Help Systems: From Helplessness to Intelligent Assistance
Delisle, S., & Moulin, B. (2002) · Artificial Intelligence Review · doi:10.1023/a:1015179704819 · Cited by 33
Generating Music from Literature
Davis, H., & Mohammad, S. M. (2014) · Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL) · doi:10.3115/v1/w14-0901 · Cited by 33
A wearable device for physical and emotional health monitoring
Murali, S., Rincón, F. J., & Atienza, D. (2015) · Computing in cardiology · doi:10.1109/cic.2015.7408601 · Cited by 33
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
