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 3,301–3,400 of 7,380
Deep affect recognition from R-R intervals
Gjoreski, M., Gjoreski, H., Luštrek, M., & Gams, M. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3125608 · Cited by 10
E-learning and blended learning in textile engineering education: a closed feedback loop approach
Charitopoulos, A. G., Vassiliadis, S. G., Rangoussi, M., & Koulouriotis, D. E. (2017) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/254/22/222002 · Cited by 10
Virtual Standardized Patients for Assessing the Competencies of Psychologists
Parsons, T. D. (2014) · Advances in information quality and management · doi:10.4018/978-1-4666-5888-2.ch637 · Cited by 10
Robots Who Care: Robotic Psychology and Robotherapy Approach.
Libin, A., & Libin, E. V. (2005) · National Conference on Artificial Intelligence · Cited by 10
A multi‐agent affective interactive MAGDM approach and its applications
Peng, C., & Su, C. (2019) · Expert Systems · doi:10.1111/exsy.12480 · Cited by 10
A neuro fuzzy system for incorporating ethics in the internet of things
Sholla, S., Mir, R. N., & Chishti, M. A. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02217-2 · Cited by 10
Affect and the Expression of Emotions on the Internet: An Overview of Current Research
Serrano-Puche, J. (2019) · Springer eBooks · doi:10.1007/978-94-024-1555-1_45 · Cited by 10
Biosignal-driven Art: Beyond biofeedback
Pérez, M. A. O., Coghlan, N., Jaimovich, J., & Knapp, R. B. (2011) · Cited by 10
Attention support in digital environments. Nine questions to be addressed
Roda, C. (2009) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2009.09.010 · Cited by 11
Yeasin, M., & Bullot, B. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556418 · Cited by 10
Um agente animado para ambientes de aprendizagem colaborativos
Reategui, E. B., Ceron, R. F., Boff, E., & Vicari, R. M. (2006) · Americanae (AECID Library) · Cited by 10
Such, M. J., Barberà, R., Poveda, R., Lois, J. M. B., Gómez, A., López, A., Bordería, J. M. C., & Sánchez, M. (2006) · Technology and Disability · doi:10.3233/tad-2006-18407 · Cited by 10
Preliminary Investigations into the Use of Wearable Computers
Baber, C., Haniff, D. J., Cooper, L., Knight, J., & Mellor, B. (1998) · Springer eBooks · doi:10.1007/978-1-4471-3605-7_20 · Cited by 10
Designing to persuade: the use of emotion in networked media
Light, A. (2004) · Interacting with Computers · doi:10.1016/j.intcom.2004.06.007 · Cited by 10
Toward merging cognition and affect in hci
de Rosis, F. (2002) · Applied Artificial Intelligence · doi:10.1080/08839510290030363 · Cited by 10
Making robots emotion-sensitive - preliminary experiments and results
Rani, P., & Sarkar, N. (2006) · IEEE RO-MAN · doi:10.1109/roman.2005.1513747 · Cited by 10
Measuring spontaneous vocal and facial emotion expressions in real world environments
Truong, K. P., Neerincx, M. A., & van Leeuwen, D. A. (2008) · Cited by 10
Determining user interests about museum collections
Rutledge, L., Aroyo, L. M., & Stash, N. V. (2006) · International World Wide Web Conference (WWW) · doi:10.1145/1135777.1135912 · Cited by 10
Unscripted Narrative for Affectively Driven Characters
Louchart, S., Aylett, R. S., Dias, J., & Paiva, A. (2005) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v1i1.18720 · Cited by 10
Context aware hand-held devices
Publications, V. (2000) · Cited by 10
To display or not to display? Towards the architecture of a Reflexive Agent
Poggi, I., Pélachaud, C., & Carolis, B. N. D. (2001) · Iris (Roma Tre University) · Cited by 10
Providing lightweight telepresence in mobile communication to enhance collaborative living
Marmasse, N. (2004) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 10
Affective Information in Human-Computer Interaction
Partala, T. (2009) · Tampere University Institutional Repository (Tampere University) · Cited by 10
Geller, T. (2014) · Communications of the ACM · doi:10.1145/2555809 · Cited by 10
Nehaniv, C. L. (1999) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 10
EDRMS search behaviour: implications for records management principles and practices
Joseph, P. (2010) · UWA Profiles and Research Repository (UWA) · Cited by 8
Mechanisms of Artistic Creativity in Deep Learning Neural NetworksPreprint
Wyse, L. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00321 · Cited by 10
Effective Knowledge Transfer in Family FirmsPreprint
Trevinyo‐Rodríguez, R. N., & Tàpies, J. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1673904 · Cited by 10
Virtual Reality for Experiential Education: A User Experience Exploration
Schott, C., & Marshall, S. J. (2020) · doi:10.26686/wgtn.13174958.v1 · Cited by 10
Silva, I. S., Guerreiro, J., Rosa, M., Campos, J., Pascoal, A. G., Pinto, S., & Nicolau, H. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376239 · Cited by 9
ULME-GAN: a generative adversarial network for micro-expression sequence generation
Zhou, J., Sun, S., Xia, H., Liu, X., Wang, H., & Chen, T. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05213-z · Cited by 11
Nalwaya, A., & Pachori, R. B. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3347648 · Cited by 9
Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
Kargarandehkordi, A., Kaisti, M., & Washington, P. Y. (2024) · Applied Sciences · doi:10.3390/app14041337 · Cited by 9
Brain-inspired modular echo state network for EEG-based emotion recognition
Yang, L., Wang, Z., Wang, G., Liang, L., Liu, M., & Wang, J. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1305284 · Cited by 15
Ten Traps for Non-Representational Theory in Human Geography
Torrens, P. M. (2024) · Geographies · doi:10.3390/geographies4020016 · Cited by 9
On quantum computing for artificial superintelligence
Grabowska, A., & Gunia, A. (2024) · European Journal for Philosophy of Science · doi:10.1007/s13194-024-00584-7 · Cited by 14
Zhu, Y., Huang, L., Chen, J., Wang, S., Wan, F., & Chen, J. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300781 · Cited by 9
Saavedra, Á., Chocarro, R., Cortiñas, M., & Rubio, N. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2504514 · Cited by 10
Nissan, E. (2007) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-2007-00352 · Cited by 9
Ramírez, C., Concha, C., & Valdés, B. (2010) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2010.5673555 · Cited by 9
Moral Coppélia - Combining Ratio with Affect in Ethical Reasoning
Pontier, M. A., Widdershoven, G. A. M., & Hoorn, J. F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34654-5_45 · Cited by 9
Spontaneous Facial Expression Recognition Based on Feature Point Tracking
He, S., Wang, S., & Lv, Y. (2011) · 2011 Sixth International Conference on Image and Graphics · doi:10.1109/icig.2011.91 · Cited by 9
Semantic audiovisual analysis for video summarization
You, J., Hannuksela, M. M., & Gabbouj, M. (2009) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurcon.2009.5167816 · Cited by 9
Happy, S. L., & Routray, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-89629-8_13 · Cited by 10
Student Emotion Recognition in Computer Science Education: A Blessing or Curse?
van der Haar, D. T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21814-0_23 · Cited by 9
Research on image sentiment analysis technology based on sparse representation
Jin, X., Wu, Y., Xu, Y., & Sun, C. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12074 · Cited by 9
Facial Expression Recognition from a Single Face Image Based on Deep Learning and Broad Learning
Bie, M., Xu, H., Gao, Y. J., & Che, X. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/7094539 · Cited by 9
Urgent topics for advertising research: addressing critical gaps in the literature
Huh, J., Xu, H., & Abdollahi, M. (2022) · International Journal of Advertising · doi:10.1080/02650487.2022.2150955 · Cited by 9
In Technology We Trust! But Should We?
Sica, A., & Sætra, H. S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35599-8_19 · Cited by 9
Emoticon recommendation in microblog using affective trajectory model
Liang, W.-B., Wang, H.-C., Chu, Y.-A., & Wu, C.-H. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference · doi:10.1109/apsipa.2014.7041632 · Cited by 9
Understanding considerate systems — UCS (pronounced: You see us)
Selker, T. (2010) · International Conference on Collaboration Technologies and Systems (CTS) · doi:10.1109/cts.2010.5478532 · Cited by 9
Prerequisites for Affective Signal Processing (ASP) – Part IV
van den Broek, E. L., van der Zwaag, M. D., Healey, J., Janssen, J. H., & Westerink, J. H. D. M. (2010) · doi:10.5220/0002813200510058 · Cited by 10
Spoken affect classification using neural networks
Morrison, D., Wang, R., & Silva, L. C. D. (2005) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2005.1547359 · Cited by 9
Words semantic orientation classification based on HowNet
Li, D., Ma, Y., & Guo, J. (2009) · 中国邮电高校学报：英文版 · doi:10.1016/s1005-8885(08)60188-0 · Cited by 9
SYSTEMS, MODELS AND SELF-AWARENESS: TOWARDS ARCHITECTURAL MODELS OF CONSCIOUSNESS
Sanz, R., Hernández, C., GÓMEZ, J., Bermejo–Alonso, J., Rodríguez, M., Hernando, A., & Sánchez-Escribano, M. G. (2009) · International Journal of Machine Consciousness · doi:10.1142/s1793843009000359 · Cited by 9
Integrating Learning Styles and Affect with an Intelligent Tutoring System
Cabada, R. Z., Estrada, M. L. B., Camacho, J. L. O., & Reyes-Garcia, C. A. (2013) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2013.36 · Cited by 9
THE B-I-C-A OF BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES
Stocco, A., Lebiere, C. J., & Samsonovich, A. V. (2010) · International Journal of Machine Consciousness · doi:10.1142/s1793843010000552 · Cited by 9
Xu, A., Biehl, J. T., Rieffel, E. G., Turner, T., & van Melle, W. (2012) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208524 · Cited by 9
Personalizing the theme park: psychometric profiling and physiological monitoring
Rennick‐Egglestone, S., Whitbrook, A. M., Tennent, P., Irune, A., Rowland, D., Leygue, C., Greensmith, J., Walker, B., Benford, S., Schnädelbach, H. M., et al. (2024) · Figshare · Cited by 9
Towards to Psychological-based Recommenders Systems: A survey on Recommender Systems
Nunes, M. A. S. N. (2010) · Scientia Plena · Cited by 9
Sentiment Analysis in the Planet Art: A Case Study in the Social Semantic Web
Baldoni, M., Cristina Baroglio, A., Patti, V., & Schifanella, C. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-31546-6_8 · Cited by 9
Semantic knowledge inference from online news media using an LDA-NLP approach
Doumit, S., & Minai, A. A. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033626 · Cited by 9
Collaborative networks approach to active ageing
Camarinha-Matos, L. M., Afsarmanesh, H., & Ferrada, F. (2010) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth2010.8866 · Cited by 9
Kaklauskas, A., Zavadskas, E. K., Seniut, M., Krutinis, M., Dzemyda, G., Ivanikovas, S., Stankevič, V., Šimkevičius, Č., & Jarusevicius, A. (2008) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.3846/isarc.20080626.69 · Cited by 9
Impact Study of Nonverbal Facial Cues on Spontaneous Chatting with Virtual Humans
Gobron, S., Ahn, J.-H., Thalmann, D., Skowron, M., & Kappas, A. (2011) · Journal of Virtual Reality and Broadcasting · doi:10.20385/1860-2037/10.2013.6 · Cited by 9
Developing an adaptive serious game based on students' bio-feedback
Katmada, A., Mavridis, A., Apostolidis, H., & Tsiatsos, Τ. (2015) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2015.7387975 · Cited by 9
Improving a Method for Quantifying Readers' Impressions of News Articles with a Regression Equation
Kumamoto, T., Kawai, Y., & Tanaka, K. (2011) · Cited by 9
Chanel, G., Pelli, S., Ravaja, N., & Kuikkaniemi, K. (2010) · Archive ouverte UNIGE (University of Geneva) · Cited by 9
Mone, G. (2015) · Communications of the ACM · doi:10.1145/2800498 · Cited by 9
Affect-sensitive Virtual Standardized Patient Interface System
Parsons, T. D. (2011) · Advances in higher education and professional development book series · doi:10.4018/978-1-60960-147-8.ch015 · Cited by 9
Computational challenges for sentiment analysis in life sciences
Ciullo, F., Zucco, C., Calabrese, B., Agapito, G., Guzzi, P. H., & Cannataro, M. (2016) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2016.7568365 · Cited by 9
On Computational Models of Emotion Regulation and Their Applications Within HCI
Bosse, T. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801851-4.00013-6 · Cited by 9
Investigating Facial Features for Identification of Emotions
Libralon, G. L., & Romero, R. A. F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42042-9_51 · Cited by 9
Feel the image: The role of emotions in the image-seeking process
Poretski, L., Lanir, J., & Arazy, O. (2017) · Human-Computer Interaction · doi:10.1080/07370024.2017.1359604 · Cited by 13
Wilson, C., DRAPER, S. W., Brereton, M. F., & Johnson, D. M. (2017) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3152771.3152802 · Cited by 9
Tamayo, J. L. R., Hernández, M. A. B., & Gómez, H. G. (2018) · Revista ICONO14 · doi:10.7195/ri14.v16i2.1174 · Cited by 10
Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach
Gilbert, S. B., Dorneich, M. C., Walton, J., & Winer, E. H. (2018) · Research on managing groups and teams · doi:10.1108/s1534-085620180000019014 · Cited by 9
A Cloud-Based Architecture for an Affective Recommender System of Learning Resources
Leony, D., Pardo, A., G., H. A. P., & Kloos, C. D. (2012) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 9
A Cloud-Based Voting System for Emotion Recognition in Human-Computer Interaction
Magyar, J., Magyar, G., & Sinčák, P. (2018) · World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490628 · Cited by 9
Emotion-Aware Systems for Promoting Human Well-being
Lascio, E. D. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3267316 · Cited by 9
Affective Interaction Design and Narrative Presentation.
Tanenbaum, T. J., & Tomizu, A. (2007) · National Conference on Artificial Intelligence · Cited by 9
Sensus Vox: Sentiment Mapping Through Smartphone Multi-Sensory Crowdsourcing
Fasoulis, A., Virvou, M. K., Tsihrintzis, G. A., Patsakis, C., & Alepis, E. (2018) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2018.00074 · Cited by 9
Pham, P. T., & Wang, J. (2019) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3232233 · Cited by 9
Zhou, Q., Newn, J., Srivastava, N., Dingler, T., Gonçalves, J., & Velloso, E. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3313010 · Cited by 9
Dhankar, R. S. (2019) · India studies in business and economics · doi:10.1007/978-81-322-3950-5_19 · Cited by 9
Applications of Character Computing From Psychology to Computer Science
Bolock, A. E., Abdennadher, S., & Herbert, C. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-15954-2_4 · Cited by 9
Triantafyllou, A. M., Tsihrintzis, G. A., Virvou, M. K., & Alepis, E. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-41196-1_2 · Cited by 9
Towards an automatic recommendation system to well-being for elderly based on augmented reality
Ronquillo, L., Zamudio, V., Gutiérrez-Hernández, D., Lino, C., Navarro, J., & Doctor, F. (2020) · Australasian Conference on Interactive Entertainment · doi:10.1109/ie49459.2020.9155010 · Cited by 5
BDIE: a BDI like architecture with emotional capabilities
Hernandez, D. J., Déniz, Ó., Lorenzo-Navarro, J., & Hernández, M. (2004) · Cited by 9
Automatic Estimation of Action Unit Intensities and Inference of Emotional Appraisals
Seuß, D., Hassan, T., Dieckmann, A., Unfried, M., Scherer, K. R., Mortillaro, M., & Garbas, J.-U. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3077590 · Cited by 11
MMTrans-MT: A Framework for Multimodal Emotion Recognition Using Multitask Learning
Shen, J., Zheng, J., & Wang, X. (2021) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci52617.2021.9435906 · Cited by 9
A Comparative Analysis of Modeling and Predicting Perceived and Induced Emotions in Sonification
Abri, F., Gutiérrez, L. F. G., Datta, P., Sears, D. R. W., Namin, A. S., & Jones, K. S. (2021) · Electronics · doi:10.3390/electronics10202519 · Cited by 9
Theory of Constructed Emotion Meets RE
Taveter, K., & Iqbal, T. (2021) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew53955.2021.00067 · Cited by 9
Zhang, S., Liu, G., & Lai, X. (2015) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2015.p0118 · Cited by 9
Essential Principles for Autonomous Robotics
Hexmoor, H. H. (2013) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00506ed1v01y201305aim021 · Cited by 23
Assistive Healthcare Robotics – Challenges in Nursing Service Innovation: Critical Review
Badr, N. G., & Dankar, M. J. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224102002 · Cited by 9
Modeling Intention, Emotion and External World in Dialogue Systems
Peng, W., Hu, Y. H., Xing, L., Xie, Y., Zhang, X., & Sun, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747565 · Cited by 9
Biosignal-based user-independent recognition of emotion and personality with importance weighting
Katada, S., & Okada, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12711-8 · Cited by 9
Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?
Calefato, F., & Lanubile, F. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3491039 · Cited by 10
Affective Response Categories—Toward Personalized Reactions in Affect-Adaptive Tutoring Systems
Schmitz-Hübsch, A., Stasch, S.-M., Becker, R., Fuchs, S., & Wirzberger, M. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.873056 · Cited by 10
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
