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 1,401–1,500 of 7,380
A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics
Karg, M. E., Jenke, R., Seiberl, W., Kühnlenz, K., Schwirtz, A., & BUSS, M. J. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349438 · Cited by 42
Almaev, T. R., Martínez, B., & Valstar, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.430 · Cited by 42
A conceptual affective design framework for the use of emotions in computer game design
de Byl, P. (2015) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2015-3-4 · Cited by 42
Affective content analysis of music emotion through EEG
Hsu, J.-L., Zhen, Y.-L., Lin, T., & Chiu, Y.-S. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0542-0 · Cited by 42
Kumar, J. A., Muniandy, B., & Wan Yahaya, W. A. J. (2019) · New Review of Hypermedia and Multimedia · doi:10.1080/13614568.2019.1596169 · Cited by 42
Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants
Nalepa, G. J., Kutt, K., Giżycka, B., Jemioło, P., & Bobek, S. (2019) · Sensors · doi:10.3390/s19112509 · Cited by 41
A Marked Point Process Filtering Approach for Tracking Sympathetic Arousal From Skin Conductance
Wickramasuriya, D. S., & Faghih, R. T. (2020) · IEEE Access · doi:10.1109/access.2020.2984508 · Cited by 43
Emotional classification of music using neural networks with the MediaEval dataset
Medina, Y. O., Beltrán, J. R., & Baldassarri, S. (2020) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01393-4 · Cited by 42
Emotion Analysis in Human–Robot Interaction
Szabóová, M., Sarnovský, M., Krešňáková, V., & Machová, K. (2020) · Electronics · doi:10.3390/electronics9111761 · Cited by 43
DECN: Dialogical emotion correction network for conversational emotion recognition
Lian, Z., Liu, B., & Tao, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.017 · Cited by 42
Effective Knowledge Transfer in Family Firms
Trevinyo‐Rodríguez, R. N., & Tàpies, J. (2006) · Edward Elgar Publishing eBooks · doi:10.4337/9781847204394.00029 · Cited by 42
Affective medicine: technology with emotional intelligence.
Picard, R. W. (2002) · PubMed · Cited by 42
Self-citationOn the Use of Virtual Animals with Artificial Fear in Virtual Environments
Delgado-Mata, C., Ibáñez, J., Bee, S., Ruiz-Rodarte, R., & Aylett, R. S. (2007) · New Generation Computing · doi:10.1007/s00354-007-0009-5 · Cited by 42
Perception of products by progressive multisensory integration. A study on hammers
Vergara, M., Mondragón, S., Sancho-Bru, J. L., & Torres, M. J. A. (2010) · Applied Ergonomics · doi:10.1016/j.apergo.2010.09.014 · Cited by 42
TOWARD SOCIALLY INTELLIGENT SERVICE ROBOTS
Wilkes, D. M., Alford, A., Pack, R. T., Rogers, T. E., Peters, R. A., & Kawamura, K. (1998) · Applied Artificial Intelligence · doi:10.1080/088395198117604 · Cited by 42
Bardzell, J., & Bardzell, S. (2008) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1358628.1358703 · Cited by 42
Construction of Multi-Mode Affective Learning System: Taking Affective Design as an Example
Lin, H.-C. K., Su, S.-H., Chao, C.-J., Hsieh, C.-Y., & Tsai, S.-C. (2016) · Educational Technology & Society · Cited by 42
A Framework for Comparing Agent Architectures
Sloman, A., & Scheutz, M. J. (2002) · Cited by 42
Munro, A., Höök, K., & Benyon, D. (1999) · Computer supported cooperative work · doi:10.1007/978-1-4471-0837-5_1 · Cited by 42
McCauley, L., & Franklin, S. (1998) · Cited by 43
Social Effects of Virtual and Robot Companions
Krämer, N. C., Pütten, A. M. R.-V. D., & Hoffmann, L. (2015) · doi:10.1002/9781118426456.ch6 · Cited by 42
Computational Emotion Models: A Thematic Review
Ojha, S., Vitale, J., & Williams, M. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-020-00713-1 · Cited by 41
Conceptual design of a user-centric smart product-service system using self-organizing map
Cong, J., Chen, C., Meng, X., Xiang, Z., & Dong, L. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101857 · Cited by 41
Essentials of General Intelligence: The Direct Path to Artificial General Intelligence
Voss, P. (2007) · Cognitive technologies · doi:10.1007/978-3-540-68677-4_4 · Cited by 41
Schaaff, K., & Adam, M. T. P. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.66 · Cited by 46
Expressive Copying Behavior for Social Agents: A Perceptual Analysis
Castellano, G., Mancini, M., Peters, C., & McOwan, P. W. (2011) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2011.2172415 · Cited by 41
Gobron, S., Ahn, J.-H., Paltoglou, G., Thelwall, M. A., & Thalmann, D. (2010) · The Visual Computer · doi:10.1007/s00371-010-0446-x · Cited by 41
Bui, T., Poel, M., Nijholt, A., & Zwiers, J. (2009) · Natural Language Engineering · doi:10.1017/s1351324908005032 · Cited by 40
Behavior change support systems: the next frontier for web science
B, H. O.-K. A. (2010) · Cited by 40
Companion-Technology: An Overview
Biundo, S., Höller, D., Schattenberg, B., & Bercher, P. (2016) · Künstliche Intell. · doi:10.1007/s13218-015-0419-3 · Cited by 41
Game theory based emotional evolution analysis for chinese online reviews
Bu, Z., Li, H., Cao, J., Wu, Z., & Zhang, L. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.03.026 · Cited by 40
Emotions during Learning with AutoTutor
D’Mello, S. K., & Graesser, A. C. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139049580.010 · Cited by 41
Taxonomy of Pathways to Dangerous Artificial Intelligence
Yampolskiy, R. V. (2016) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · Cited by 41
Xue, Y., Hamada, Y., & Akagi, M. (2018) · Speech Communication · doi:10.1016/j.specom.2018.06.006 · Cited by 38
Towards emotion recognition from contextual information using machine learning
o, M., Rodríguez, L.-F., & Gutiérrez-García, J. O. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01485-x · Cited by 43
Neural Network Based Emotion Estimation Using Heart Rate Variability and Skin Resistance
Yoo, S. K., Lee, C. K. K., Park, Y. J., Kim, N. H., Lee, B. C., & Jeong, K. (2005) · Lecture notes in computer science · doi:10.1007/11539087_110 · Cited by 41
Emotion development system by interacting with human EEG and natural scene understanding
Zhang, Q., & Lee, M. (2011) · Cognitive Systems Research · doi:10.1016/j.cogsys.2010.12.012 · Cited by 43
Zhao, Y., Wang, X., Goubran, M., Whalen, T. E., & Petriu, E. M. (2012) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-012-0107-1 · Cited by 41
Who Needs Stories if You Can Get the Data? ISPs in the Era of Big Number Crunching
Hildebrandt, M. (2011) · Philosophy & Technology · doi:10.1007/s13347-011-0041-8 · Cited by 43
Wu, Q., & Miao, C. (2013) · ACM Computing Surveys · doi:10.1145/2543581.2543585 · Cited by 44
User-oriented affective video content analysis
Hanjalić, A., & Xu, L.-Q. (2002) · IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL) · doi:10.1109/ivl.2001.990856 · Cited by 40
User Study, Analysis, and Usable Security of Passwords Based on Digital Objects
Biddle, R., Mannan, M., van Oorschot, P. C., & Whalen, T. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2116781 · Cited by 41
A recipe based on-line food store
Svensson, M. G. A., Laaksolahti, J., Höök, K., & Wærn, A. (2000) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/325737.325866 · Cited by 41
A tangibility approach to affective interaction
Wensveen, S. S. (2005) · Research Repository (Delft University of Technology) · doi:10.6100/ir807967 · Cited by 41
Towards tutoring systems that detect students' motivation: an investigation
de Vicente, Á. (2003) · ERA · Cited by 41
Pilot Study of Aurora, a Social, Mobile-Phone-Based Emotion Sharing and Recording System
Gay, G., Pollak, J. P., Adams, P., & Leonard, J. P. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500219 · Cited by 40
A cognitive theory for affective user modelling in a virtual reality educational game
Katsionis, G., & Virvou, M. K. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1399789 · Cited by 40
The Effects of Emotions on Trust in Human-Computer Interaction: A Survey and Prospect
Jeon, M. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2261727 · Cited by 46
Educational Robots, Emotion Recognition and ASD: New Horizon in Special Education
Schiavo, F., Campitiello, L., Todino, M. D., & Tore, P. A. D. (2024) · Education Sciences · doi:10.3390/educsci14030258 · Cited by 48
Toward a Taxonomy of Affective Computing
Schwark, J. (2015) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2015.1064638 · Cited by 41
Stress inoculation training supported by physiology-driven adaptive virtual reality stimulation.
Popović, S., Horvat, M., Kukolja, D., Dropuljić, B., & Ćosić, K. (2009) · PubMed · Cited by 40
Can a Humanoid Face be Expressive? A Psychophysiological Investigation
Lazzeri, N., Mazzei, D., Greco, A., Rotesi, A., Lanatà, A., & Rossi, D. E. D. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00064 · Cited by 40
Mapping discrete emotions into the dimensional space: An empirical approach
Hoffmann, H., Scheck, A., Schuster, T., Walter, S., Limbrecht, K., Traue, H. C., & Kessler, H. (2012) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2012.6378303 · Cited by 38
Ekman, I. M., Poikola, A., & Mäkäräinen, M. (2008) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1358628.1358820 · Cited by 41
EEG based emotion recognition system using MFDFA as feature extractor
Paul, S., Mazumder, A., Ghosh, P., Tibarewala, D. N., & Vimalarani, G. (2015) · International Conference on Robotics, Automation, Control and Embedded Systems (RACE) · doi:10.1109/race.2015.7097247 · Cited by 40
Betella, A., Zucca, R., Cetnarski, R., Greco, A., Lanatà, A., Mazzei, D., Tognetti, A., Arsiwalla, X. D., Omedas, P., Rossi, D. D., et al. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00286 · Cited by 41
Intelligent human interface based on mental cloning-based software
Fujita, H., Hakura, J., & Kurematu, M. (2008) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.11.005 · Cited by 40
Rázuri, J. G., Sundgren, D., Rahmani, R., & Morán, A. (2013) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2013.16 · Cited by 40
Emotion Recognition for hHman-Machine Communication
Maaoui, C., Pruski, A., & Abdat, F. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650870 · Cited by 40
Higher-order Multivariable Polynomial Regression to Estimate Human Affective States
Wei, J., Chen, T., Liu, G., & Yang, J. (2016) · Scientific Reports · doi:10.1038/srep23384 · Cited by 41
An affect detection technique using mobile commodity sensors in the wild
Mottelson, A., & Hornbæk, K. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971654 · Cited by 40
Designing the Mind of a Social Robot
Lazzeri, N., Mazzei, D., Cominelli, L., Cisternino, A., & Rossi, D. E. D. (2018) · Applied Sciences · doi:10.3390/app8020302 · Cited by 40
Wulvik, A. S., Dybvik, H., & Steinert, M. (2019) · Cognition Technology & Work · doi:10.1007/s10111-019-00553-8 · Cited by 45
Affective Sleeve: Wearable Materials with Haptic Action for Promoting Calmness
Papadopoulou, A., Berry, J., Knight, T., & Picard, R. W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21935-2_23 · Cited by 42
Self-citationAslan, S., Güdükbay, U., & Dibeklioğlu, H. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104163 · Cited by 40
Feel the Difference: A Guide with Attitude!
Lim, M., & Aylett, R. (2007) · International Conference on Intelligent Virtual Agents · doi:10.1007/978-3-540-74997-4_29 · Cited by 46
Self‐service technology complaint channel choice
Robertson, N. L. (2012) · Managing Service Quality · doi:10.1108/09604521211218963 · Cited by 39
Cortés, U., Annicchiarico, R., Vázquez-Salceda, J., Urdiales, C., Cañamero, L. D., López-Sánchez, M., Sànchez–Marrè, M., & Caltagirone, C. (2003) · Cited by 40
Arifin, S., & Cheung, P. Y. K. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291251 · Cited by 40
The scope of application of multi-agent systems in the process industry: three case studies
Aldea, A. (2003) · Expert Systems with Applications · doi:10.1016/s0957-4174(03)00105-2 · Cited by 40
Implementing moral decision making faculties in computers and robots
Wallach, W. (2007) · AI & Society · doi:10.1007/s00146-007-0093-6 · Cited by 50
Wu, C.-K., Chung, P., & Wang, C.-J. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.14 · Cited by 40
Modeling the Interplay Between Emotion and Decision-Making .
Gratch, J. S. (2000) · Cited by 40
AFFECT AND AGENT CONTROL: EXPERIMENTS WITH SIMPLE AFFECTIVE STATES
Scheutz, M. J., & Sloman, A. (2001) · IEEE/WIC/ACM International Conference on Intelligent Agent Technology · doi:10.1142/9789812811042_0028 · Cited by 40
Integrating emotional cues into a framework for dialogue management
Holzapfel, H., Fügen, C., Denecke, M., & Waibel, A. (2003) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1109/icmi.2002.1166983 · Cited by 40
Sensei, a real-time recognition, feedback and training system for T'ai chi gestures
Becker, D. (1997) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 40
Beyond usability: measuring aspects of user experience
Zimmermann, P. (2008) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-005778404 · Cited by 39
About Face: A Survey of Facial Recognition EvaluationPreprint
Raji, I. D., & Fried, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00813 · Cited by 40
The Multipurpose Enhanced Cognitive Architecture (MECA)
Gudwin, R., Paraense, A. L. O., de Paula, S. M., Froes, E. D. M., Gibaut, W., Castro, E., Figueiredo, V. L. X., & Raizer, K. (2017) · Biologically Inspired Cognitive Architectures · doi:10.1016/j.bica.2017.09.006 · Cited by 39
The semantic pointer theory of emotion: Integrating physiology, appraisal, and construction
Kajić, I., Schröder, T., Stewart, T. C., & Thagard, P. R. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.04.007 · Cited by 39
Empowering virtual collaboration: harnessing AI for enhanced teamwork in higher education
Jony, A. I., & Hamim, S. A. (2024) · Educational Technology Quarterly · doi:10.55056/etq.746 · Cited by 51
MLM-EOE: Automatic Depression Detection via Sentimental Annotation and Multi-Expert Ensemble
Lin, Z., Wang, Y., Zhou, Y., Du, F., & Yang, Y. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3585599 · Cited by 43
Blocher, K. H., & Picard, R. W. (2002) · Multiagent systems, artificial societies, and simulated organizations · doi:10.1007/0-306-47373-9_16 · Cited by 39
Self-citationCohesion Relationships in Tutorial Dialogue as Predictors of Affective States
D'Mello, S., Dowell, N., & Graesser, A. C. (2009) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-028-5-9 · Cited by 39
Development of a web-based survey system for evaluating affective satisfaction
Cho, Y., Park, J., Han, S. H., & Kang, S. (2011) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2011.01.009 · Cited by 39
A System for Affect Analysis of Utterances in Japanese Supported with Web Mining
Ptaszynski, M. E., Dybała, P., Shi, W., Rzepka, R., & Araki, K. (2009) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.21.194 · Cited by 39
A survey of technologies on the rise for emotion-enhanced interaction
Cernea, D., & Kerren, A. (2015) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2015.10.001 · Cited by 39
Blood Cell Identification Using Emotional Neural Networks
Khashman, A. (2009) · Cited by 39
Fundamental frequency modeling using wavelets for emotional voice conversion
Ming, H., Huang, D., Dong, M., Li, H., Xie, L., & Zhang, S. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344665 · Cited by 39
Brain-Computer Interfacing and Virtual Reality
Friedman, D. A. (2015) · Springer eBooks · doi:10.1007/978-981-4560-52-8_2-1 · Cited by 39
Ubiquitous computer aided design: A broken promise or a Sleeping Beauty?
Horváth, I., & Vroom, R. W. (2014) · Computer-Aided Design · doi:10.1016/j.cad.2014.10.006 · Cited by 42
Two‐person activity recognition using skeleton data
Manzi, A., Fiorini, L., Limosani, R., Dario, P., & Cavallo, F. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0118 · Cited by 40
Emotional Human-Machine Conversation Generation Based on Long Short-Term Memory
Sun, X. N., Peng, X., & Ding, S. (2017) · Cognitive Computation · doi:10.1007/s12559-017-9539-4 · Cited by 39
Affective patterns in serious games
Argasiński, J. K., & Wȩgrzyn, P. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.06.013 · Cited by 43
Machine learning approaches for boredom classification using EEG
Seo, J., Laine, T. H., & Sohn, K.-A. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01196-3 · Cited by 39
Emotion Recognition Using Fused Physiological Signals
Fabiano, D., & Canavan, S. J. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925486 · Cited by 39
Huang, Y., Li, J., Wu, G., & Fei, T. (2020) · Cities · doi:10.1016/j.cities.2020.102719 · Cited by 38
von Braun, J., Archer, M. S., Reichberg, G. M., & Sorondo, M. S. (2021) · Springer eBooks · doi:10.1007/978-3-030-54173-6 · Cited by 44
Can You Hear My Heartbeat?: Hearing an Expressive Biosignal Elicits Empathy
Winters, R. M., Walker, B. N., & Leslie, G. A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445545 · Cited by 43
Atas, M., Felfernig, A., Erdeniz, S. P., Popescu, A., Tran, T. N. T., & Uta, M. (2021) · Journal of Intelligent Information Systems · doi:10.1007/s10844-021-00674-5 · Cited by 47
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