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 701–800 of 7,380
Fabri, M., Moore, D., & Hobbs, D. J. (2004) · Virtual Reality · doi:10.1007/s10055-003-0116-7 · Cited by 97
Telemetric monitoring in the behavior sciences
Goodwin, M. S., Velicer, W. F., & Intille, S. S. (2008) · Behavior Research Methods · doi:10.3758/brm.40.1.328 · Cited by 91
From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities
Güneş, H., Piccardi, M., & Pantić, M. (2008) · doi:10.5772/6180 · Cited by 95
Computers as persuasive social actors
Fogg, B. J. (2003) · Elsevier eBooks · doi:10.1016/b978-155860643-2/50007-x · Cited by 94
Review of Semantic-Free Utterances in Social Human–Robot Interaction
Yilmazyildiz, S., Read, R., Belpeame, T., & Verhelst, W. (2015) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2015.1093856 · Cited by 95
A Comprehensive Study of Emotional Responses in AI-Enhanced Interactive Installation Art
Chen, X., & Ibrahim, Z. (2023) · Sustainability · doi:10.3390/su152215830 · Cited by 111
Remarks on emotion recognition from multi-modal bio-potential signals
Takahashi, K. (2005) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2004.1490720 · Cited by 91
Effects of Web Atmospheric Cues on Users’ Emotional Responses in E-Commerce
Sheng, H., & Joginapelly, T. (2012) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00036 · Cited by 92
Emotional brain-computer interfaces
Garcia‐Molina, G., Tsoneva, T., & Nijholt, A. (2012) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2013.050687 · Cited by 91
The Digital Therapeutic Alliance and Human-Computer Interaction
D'Alfonso, S., Lederman, R., Bucci, S., & Berry, K. (2020) · JMIR Mental Health · doi:10.2196/21895 · Cited by 90
Classification of emotions induced by music videos and correlation with participants’ rating
Daimi, S. N., & Saha, G. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.03.050 · Cited by 90
Wagner, K., Nimmermann, F., & Schramm‐Klein, H. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.169 · Cited by 90
The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues
Hasan, M. A., Noor, N. F. B. M., Rahman, S. S. B. A., & Rahman, M. M. (2020) · IEEE Access · doi:10.1109/access.2020.3036990 · Cited by 103
Promises and trust in human–robot interaction
Cominelli, L., Feri, F., Garofalo, R., Giannetti, C., Meléndez-Jímenez, M. A., Greco, A., Nardelli, M., Scilingo, E. P., & Kirchkamp, O. (2021) · Scientific Reports · doi:10.1038/s41598-021-88622-9 · Cited by 91
Garcia-Garcia, J. M., Penichet, V. M. R., Lozano, M. D., & Fernando, A. (2021) · Universal Access in the Information Society · doi:10.1007/s10209-021-00818-y · Cited by 90
Affective Interactions: Towards a New Generation of Computer Interfaces
Paiva, A. (2000) · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · doi:10.1007/10720296 · Cited by 90
Batliner, A. M., Buckow, J., Niemann, H., Nöth, E., & Warnke, V. (2000) · Artificial intelligence · doi:10.1007/978-3-662-04230-4_8 · Cited by 90
Khezri, M., Firoozabadi, M., & Sharafat, A. R. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.07.006 · Cited by 89
Diagnosing and acting on student affect: the tutor’s perspective
Porayska‐Pomsta, K., Mavrikis, M., & Pain, H. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9041-x · Cited by 89
Multimodal Deep Learning Framework for Mental Disorder Recognition
Zhang, Z., Lin, W., Liu, M., & Mahmoud, M. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00033 · Cited by 89
Using the Community of Inquiry Framework to Inform Effective Instructional Design
Richardson, J. C., Arbaugh, J. B., Cleveland-Innes, M. F., Ice, P., Swan, K. P., & Garrison, D. R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-1785-9_7 · Cited by 108
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Triantafyllopoulos, A., Schuller, B. W., İymen, G., Sezgin, T. M., He, X., Yang, Z., Tzirakis, P., Liu, S., Mertes, S., André, E., et al. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3250266 · Cited by 102
Li, C., Tang, T., Pan, Y., Yang, L., Zhang, S., Chen, Z., Li, P., Gao, D., Chen, H., Li, F., et al. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3405663 · Cited by 92
Aerospace launch vehicle control: an intelligent adaptive approach
Mehrabian, A. R., Lucas, C., & Roshanian, J. (2005) · Aerospace Science and Technology · doi:10.1016/j.ast.2005.11.002 · Cited by 89
Understanding how bloggers feel
Leshed, G., & Kaye, J. (2006) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1125451.1125646 · Cited by 89
Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications
Bisio, I., Delfino, A., Lavagetto, F., Marchese, M., & Sciarrone, A. (2013) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2013.2274797 · Cited by 89
Affection not affliction: The role of emotions in information systems and organizational change
McGrath, K. (2006) · Information and Organization · doi:10.1016/j.infoandorg.2006.09.001 · Cited by 89
Stress detection in computer users through non-invasive monitoring of physiological signals.
Zhai, J., & Barreto, A. B. (2006) · PubMed · Cited by 89
Lottridge, D. M., Chignell, M., & Jovičić, A. (2011) · Reviews of Human Factors and Ergonomics · doi:10.1177/1557234x11410385 · Cited by 89
Analyzing the emotional outcomes of the online search behavior with search engines
Flavián, C., Gurrea, R., & Orús, C. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.10.002 · Cited by 88
EEG Emotion Recognition Based on Graph Regularized Sparse Linear Regression
Li, Y., Zheng, W., Cui, Z., Zong, Y., & Ge, S. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9829-1 · Cited by 88
Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia
Raheel, A., Majid, M., Alnowami, M. R., & Anwar, S. M. (2020) · Sensors · doi:10.3390/s20144037 · Cited by 88
Affective ranking of movie scenes using physiological signals and content analysis
Soleymani, M., Chanel, G., Kierkels, J. J. M., & Pun, T. (2008) · doi:10.1145/1460676.1460684 · Cited by 88
Robot Learning Driven by Emotions
Gadanho, S. C., & Hallam, J. C. T. (2001) · Adaptive Behavior · doi:10.1177/105971230200900102 · Cited by 90
Tollmar, K., & Persson, J. (2002) · Nordic Conference on Human-Computer Interaction · doi:10.1145/572020.572027 · Cited by 88
Affective Content Analysis in Comedy and Horror Videos by Audio Emotional Event Detection
Xu, M. G., Chia, L.-T., & Jin, J. S. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2005.1521500 · Cited by 88
Höök, K., Ståhl, A., Sundström, P., & Laaksolahti, J. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357157 · Cited by 88
System Design for Big Data Application in Emotion-Aware Healthcare
Lin, K., Xia, F., Wang, W., Tian, D., & Song, J. (2016) · IEEE Access · doi:10.1109/access.2016.2616643 · Cited by 88
Velicer, W. F., & Fava, J. L. (2003) · Wiley eBooks · doi:10.1002/0471264385.wei0223 · Cited by 95
Emotional Aspects of Computer-based Provider Order Entry: A Qualitative Study
Sittig, D. F. (2005) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1711 · Cited by 87
Varieties of Affect and the CogAff Architecture Schema
Sloman, A. (2001) · Cited by 87
Wired Emotions: Ethical Issues of Affective Brain–Computer Interfaces
Steinert, S., & Friedrich, O. (2019) · Science and Engineering Ethics · doi:10.1007/s11948-019-00087-2 · Cited by 89
Weber‐Guskar, E. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09598-8 · Cited by 87
Do physiological data relate to traditional usability indexes
Lin, T., Omata, M., Hu, W., & Imamiya, A. (2005) · doi:10.5555/1108368.1108405 · Cited by 87
Lin, H.-C. K., Wu, C., & Hsueh, Y.-P. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.052 · Cited by 92
Anxiety detection during human-robot interaction
Kulić, D., & Croft, E. A. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1545012 · Cited by 87
Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2187647 · Cited by 87
Toward a decision-theoretic framework for affect recognition and user assistance
Liao, W., Zhang, W., Zhu, Z., Ji, Q., & Gray, W. D. (2006) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.04.001 · Cited by 90
Editing by Viewing: Automatic Home Video Summarization by Viewing Behavior Analysis
Peng, W., Chu, W.-T., Chang, C.-H., Chou, C.-N., Huang, W., Chang, W.-Y., & Hung, Y. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2131638 · Cited by 87
The language of emotion in short blog texts
Gill, A. J., French, R. M., Gergle, D., & Oberlander, J. (2008) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/1460563.1460612 · Cited by 87
Rezazadeh, I. M., Wang, X., Firoozabadi, M., & Golpayegani, M. R. (2010) · Automation in Construction · doi:10.1016/j.autcon.2010.10.005 · Cited by 87
Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques
Balan, O. C., Moise, G., Moldoveanu, A. D. B., Leordeanu, M., & Moldoveanu, F. (2019) · Sensors · doi:10.3390/s19071738 · Cited by 88
Digital creativity: Research themes and framework
Lee, M. R., & Chen, T. T. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.001 · Cited by 90
A GPSO-optimized convolutional neural networks for EEG-based emotion recognition
Gao, Z., Li, Y., Yang, Y., Wang, X., Dong, N., & Chiang, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.096 · Cited by 88
Introduction to Human Affective Neuroscience
Armony, J. L., & Vuilleumier, P. O. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9780511843716.003 · Cited by 96
WASABI: affect simulation for agents with believable interactivity
Becker-Asano, C. (2008) · Cited by 86
Moghimi, S., Kushki, A., Power, S., Guerguerian, A. M., & Chau, T. T. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/2/026022 · Cited by 84
Simulation of the Dynamics of Nonplayer Characters' Emotions and Social Relations in Games
Ochs, M., Sabouret, N., & Corruble, V. (2009) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2009.2036247 · Cited by 98
Enhancing ubiquitous computing with user interpretation
Gaver, W. W., Sengers, P., Kerridge, T., Kaye, J., & Bowers, J. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240711 · Cited by 86
Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications
Benamara, F., Taboada, M., & Mathieu, Y. Y. (2016) · Computational Linguistics · doi:10.1162/coli_a_00278 · Cited by 98
Techniques for Modeling Human Performance in Synthetic Environments: A Supplementary Review
Ritter, F. E., Shadbolt, N. R., Elliman, D. G., Young, R. M., Gobet, F. R., & Baxter, G. D. (2003) · Defense Technical Information Center (DTIC) · doi:10.21236/ada487721 · Cited by 86
Perceiving blocks of emotional pictures and sounds: effects on physiological variables.
Brouwer, A.-M., van Wouwe, N., Mühl, C., van Erp, J., & Toet, A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00295 · Cited by 85
On Proactive, Transparent, and Verifiable Ethical Reasoning for Robots
Bremner, P., Dennis, L. A., Fisher, M., & Winfield, A. F. T. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2898267 · Cited by 85
Facial Micro-Expressions: An Overview
Zhao, G., Li, X., Li, Y., & Pietikäinen, M. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3275192 · Cited by 97
The HandWave Bluetooth Skin Conductance Sensor
Strauss, M. D., Reynolds, C., Hughes, S., Park, K., McDarby, G., & Picard, R. W. (2005) · Lecture notes in computer science · doi:10.1007/11573548_90 · Cited by 85
Self-citationFramework for Electroencephalography-based Evaluation of User Experience
Frey, J., Daniel, M., Castet, J., Hachet, M., & Lotte, F. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858525 · Cited by 82
From tags to emotions: Ontology-driven sentiment analysis in the social semantic web
Baldoni, M., Cristina Baroglio, A., Patti, V., & Rena, P. (2012) · Intelligenza Artificiale · doi:10.3233/ia-2012-0028 · Cited by 84
Toombs, A. L., Gross, S., Bardzell, S., & Bardzell, J. (2016) · Interacting with Computers · doi:10.1093/iwc/iww010 · Cited by 84
Filntisis, P. P., Efthymiou, N., Koutras, P., Potamianos, G., & Maragos, P. A. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2930434 · Cited by 90
Shaping the Stream: Techniques and Troubles of Algorithmic Recommendation
Goldschmitt, K. E., & Seaver, N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316676639.006 · Cited by 85
Boehner, K., Sengers, P., & Warner, S. (2008) · ACM Transactions on Computer-Human Interaction · doi:10.1145/1453152.1453155 · Cited by 84
Tian, F., Gao, P., Li, L., Zhang, W., Liang, H., Qian, Y., & Zhao, R. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.10.019 · Cited by 87
Stress Recognition Using Non-invasive Technology.
Zhai, J., & Barreto, A. B. (2006) · Cited by 84
Continuous emotion detection in response to music videos
Soleymani, M., Koelstra, S., Patras, I., & Pun, T. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771352 · Cited by 84
Simm, W., Ferrario, M. A., Gradinar, A., Smith, M. T., Forshaw, S., Smith, I., & Whittle, J. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858259 · Cited by 88
Cultural commentators: Non-native interpretations as resources for polyphonic assessment
Gaver, W. W. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.11.014 · Cited by 84
EmoHeart: Conveying Emotions in Second Life Based on Affect Sensing from Text
Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2010) · Advances in Human-Computer Interaction · doi:10.1155/2010/209801 · Cited by 83
Moraveji, N., Olson, B., Nguyen, T., Saadat, M., Khalighi, Y., Pea, R. D., & Heer, J. (2011) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2047196.2047250 · Cited by 83
Affect in Embodied Pedagogical Agents
Guo, Y. R., & Goh, D. H. (2015) · Journal of Educational Computing Research · doi:10.1177/0735633115588774 · Cited by 86
EMOTIC: Emotions in Context Dataset
Kosti, R., Álvarez, J. M., Recasens, A., & Lapedriza, À. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.285 · Cited by 91
Lorenzo, R. D., Venturelli, G., Spiga, G., & Ferri, P. (2019) · Acta bio-medica dell'Ateneo parmense/Acta biomedica · doi:10.23750/abm.v90i4-s.8273 · Cited by 96
Human-centred artificial intelligence: a contextual morality perspective
van Berkel, N., Tag, B., Gonçalves, J., & Hosio, S. J. (2020) · Behaviour and Information Technology · doi:10.1080/0144929x.2020.1818828 · Cited by 90
Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks
Alnuaim, A. A., Zakariah, M., Alhadlaq, A., Shashidhar, C., Hatamleh, W. A., Tarazi, H., Shukla, P. K., & Ratna, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7463091 · Cited by 83
Agent based affective tutoring systems: A pilot study
Mao, X., & Li, Z. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.01.005 · Cited by 83
Shin, D. (2009) · International Journal of Human-Computer Interaction · doi:10.1080/10447310902963951 · Cited by 83
RetractedMurray, K. B., & Häubl, G. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2009.02.009 · Cited by 88
AFFECTIVE COMPUTING FOR GAME DESIGN
Hudlická, E. (2008) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 83
Social interactions by visual focus of attention in a three‐dimensional environment
Bazzani, L., Cristani, M., Tosato, D., Farenzena, M., Paggetti, G., Menegaz, G., & Murino, V. (2012) · Expert Systems · doi:10.1111/j.1468-0394.2012.00622.x · Cited by 82
Brainball - using brain activity for cool competition
Hjelm, S. I., & Browall, C. (2000) · Cited by 83
Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System
Cambria, E., & Hussain, A. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9145-4 · Cited by 82
Read, S. J., Miller, L. C., Appleby, P. R., Nwosu, M. E., Reynaldo, S., Lauren, A., & Putcha-Bhagavatula, A. (2006) · Human Communication Research · doi:10.1111/j.1468-2958.2006.00001.x · Cited by 83
A Bayesian framework for video affective representation
Soleymani, M., Kierkels, J. J. M., Chanel, G., & Pun, T. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349563 · Cited by 82
Facial Expression Emotion Detection for Real-Time Embedded Systems
Turabzadeh, S., Meng, H., Swash, M. R., Pleva, M., & Juhár, J. (2018) · Technologies · doi:10.3390/technologies6010017 · Cited by 81
Balan, O. C., Moise, G., Moldoveanu, A. D. B., Leordeanu, M., & Moldoveanu, F. (2020) · Sensors · doi:10.3390/s20020496 · Cited by 83
4DME: A Spontaneous 4D Micro-Expression Dataset With Multimodalities
Li, X., Cheng, S., Li, Y., Behzad, M., Shen, J., Zafeiriou, S. P., Pantić, M., & Zhao, G. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3182342 · Cited by 84
Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices
Rashid, N., Mortlock, T., & Faruque, M. A. A. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3265768 · Cited by 99
Early Prediction of Student Frustration
McQuiggan, S. W., Lee, S., & Lester, J. C. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_61 · Cited by 82
Wireless personal area networks in telemedical environment
Jovanov, E., Price, J., Raŝković, D., Kavi, K. M., Martin, T. L., & Adhami, R. R. (2002) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2000.892342 · Cited by 82
Psychophysiological signals associated with affective states
Mauri, M., Magagnin, V., Cipresso, P., Mainardi, L. T., Brown, E. N., Cerutti, S., Villamira, M. A., & Barbieri, R. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627465 · Cited by 82
Anxiety-based affective communication for implicit human–machine interaction
Rani, P., Sarkar, N., & Adams, J. A. (2007) · Advanced Engineering Informatics · doi:10.1016/j.aei.2006.11.009 · Cited by 82
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
