Cited by 7,382Emotion and Mood RecognitionInnovative Human-Technology InteractionPsychiatry, Mental Health, Neuroscience

The DOI matches its Crossref record.

Affective Computing

Open access

Picard, Rosalind W.·1997·MIT Press

DOI 10.7551/mitpress/1140.001.0001

Open this paper in CoChat — free account

Abstract

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.

Integrity signals

Registry

Crossref · 10.7551/mitpress/1140.001.0001

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Sorted by citations

Items 4,101–4,200 of 7,380

Predicting emotional responses in interactive art using Random Forests: a model grounded in enactive aesthetics
Chen, X., Ibrahim, Z., & Aziz, A. A. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1609103 · Cited by 6
AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI
Singh, P., Gupta, A., Kumar, M. J., & Singh, P. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749519 · Cited by 9
Establishing a real-time biomarker-to-LLM interface: a modular pipeline for HRV signal acquisition, processing, and physiological state interpretation via generative AI
Gellisch, M., & Burr, B. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1670464 · Cited by 7
AI affective computing and behavioral health
Farsadaki, V., & Griffy‐Brown, C. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1692728 · Cited by 9
Formal and computational foundations for implementing Affective Sovereignty in emotion AI systems
Kim, R. S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01000-0 · Cited by 6
BL: A Visual Computing Framework for Interactive Neural System Models of Embodied Cognition and Face to Face Social Learning
Sagar, M. A., Robertson, P., Bullivant, D. P., Efimov, O., Jawed, K., Kalarot, R., & Wu, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21819-9_5 · Cited by 5
Facilitating Safe Adaptation of Interactive Agents using Interactive Reinforcement Learning
Tsiakas, K. (2016) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2876456.2876457 · Cited by 5
Sensay analyticstm: A real-time speaker-state platform
Tsiartas, A., Albright, C., Bassiou, N., Frandsen, M., Miller, I., Shriberg, E. E., Smith, J., Voss, L., & Wagner, V. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.8005306 · Cited by 5
Robot Dream
Tchitchigin, A. D., Talanov, M. O., Safina, L., & Mazzara, M. (2016) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-39883-9_24 · Cited by 5
HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer
Kim, Y., Lee, D. W., Liang, P. P., Alghowinem, S., Breazeal, C., & Park, H. W. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3614122 · Cited by 5
Analysis of Emotional Deconstruction and the Role of Emotional Value for Learners in Animation Works Based on Digital Multimedia Technology
Liang, S. (2023) · IET Software · doi:10.1049/2023/5566781 · Cited by 8
Modeling Player Personality Factors from In-Game Behavior and Affective Expression
Habibi, R., Pfau, J., & El‐Nasr, M. S. (2023) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw59127.2023.10388138 · Cited by 7
Continuous Emotion Ambiguity Prediction: Modeling With Beta Distributions
Bose, D., Sethu, V., & Eliathamby Ambikairajah, A. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3367371 · Cited by 5
Quantum Computational Modeling for Affective Assessment in Virtual Reality Systems
Bayro, A., & Jeong, H. (2024) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw62533.2024.00054 · Cited by 5
Multimodal Interfaces for Emotion Recognition: Models, Challenges and Opportunities
Greco, D., Barra, P., D’Errico, L., & Staffa, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60611-3_11 · Cited by 6
Real-Time Facial Expression Recognition Using Convolutional Neural Networks for Adaptive User Interfaces
Sundi, J. J., Kumar, H., Bharti, & Bedi, R. (2024) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet61516.2024.10593062 · Cited by 5
Decoding the silent language: A framework for advanced visual emotion recognition to enhance worker well-being on IIoT-enabled manufacturing
Rezaei, N., Vaezi, S. A., & Camelio, J. A. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.162 · Cited by 5
AI in Education
Kaiser, G., & Kaiser, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6130-6.ch010 · Cited by 5
Affective Matter: designing human-material interactions to enhance health and wellbeing
Papadopoulou, A. (2024) · Design Science · doi:10.1017/dsj.2024.40 · Cited by 5
Conceptualizing cognitive and physical worker-technology symbiosis in manufacturing: Lessons from J.A.R.V.I.S.
Moghaddam, M., & Klumpp, M. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.01.128 · Cited by 5
EmoFusionNet: A unified approach for robust speech emotion recognition
Vijayan, B., & Judy, M. V. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105173 · Cited by 5
UniC: a dataset for emotion analysis of videos with multimodal and unimodal labels
Du, Q., Labat, S., Demeester, T., & Hoste, V. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09837-0 · Cited by 5
Embodied Intelligence
Huang, G. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_8 · Cited by 6
Refining medical large language models: key insights from instruction tuning
Alqahtani, M. Q., Al-Barakati, A. A., & Alotaibi, F. S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3216 · Cited by 7
Maxine: Embodied conversational agents for multimodal affective communication
Baldassarri, S., & Cerezo, E. (2012) · InTech eBooks · doi:10.5772/36205 · Cited by 5
Multilevel emotion modeling for autonomous agents
Lisetti, C. (2004) · National Conference on Artificial Intelligence · Cited by 5
Depression Detection & Emotion Classification via Data-Driven Glottal Waveforms
Vandyke, D. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.112 · Cited by 5
Robust facial expression recognition using improved sparse classifier
Zhang, S., Zhang, G., & Zhao, X. (2015) · International Journal of Computer Applications in Technology · doi:10.1504/ijcat.2015.071420 · Cited by 5
Spare Me: Towards an Empathetic Tool for Helping Adolescents & Teenagers Cope with Sickle Cell
Washington, G. J., Ward, J., & Kameka, M. (2015) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2015.104 · Cited by 5
An Approach for Emotion Recognition using Purely Segment-Level Acoustic Features
Zhang, H., Warisawa, S. '. I., & Yamada, I. (2014) · doi:10.13140/2.1.1510.4646 · Cited by 5
Enhanced Affective Factors Management for HEI Students Dropout Prevention
y Restrepo, E. G., Luís-Ferreira, F., Boticario, J. G., Marcelino-Jesus, E., Sarraipa, J., & Jardim‐Gonçalves, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39483-1_61 · Cited by 6
Autonomy is the key
Ojuroye, O., Torah, R. N., Beeby, S., & Wilde, A. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968558 · Cited by 5
Fusion Architectures for Multimodal Cognitive Load Recognition
Kindsvater, D., Meudt, S., & Schwenker, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59259-6_4 · Cited by 5
Modeling Perceivers Neural-Responses Using Lobe-Dependent Convolutional Neural Network to Improve Speech Emotion Recognition
Wu, Y.-T., Chen, H.-Y., Liao, Y.-H., Kuo, L., & Lee, C.-C. (2017) · Interspeech · doi:10.21437/interspeech.2017-562 · Cited by 6
Interpersonal Coevolution of Body Movements in Daily Face-to-Face Communication
Ogata, T., Higo, N., Nozawa, T., Ono, E., Yano, K., Ara, K., & Miyake, Y. (2017) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edp7444 · Cited by 6
Simulation of serotonin mechanisms in NEUCOGAR cognitive architecture
Talanov, M. O., Gafarov, F. M., Vallverdú, J., Ostapenko, S., Gazizov, M., Toschev, A. S., Leukhin, A., & Distefano, S. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.01.072 · Cited by 5
Recognition of Positive, Negative and Neutral Emotions Using Brain Connectivity Patterns
Nematollahi, J., & Firoozabadi, M. (2017) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2017.8430281 · Cited by 4
Gamer emotions in laughter
O'Hagan, M., & Flanagan, M. (2018) · Translation Cognition & Behavior · doi:10.1075/tcb.00013.oha · Cited by 5
Improving Emotion Detection with Sub-clip Boosting
Toto, E., Foley, B. J., & Rundensteiner, E. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10997-4_3 · Cited by 5
The Impact of Image Resolution on Facial Expression Analysis with CNNs
Abbas, A., & Chalup, S. K. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852264 · Cited by 5
Association of Game Events with Facial Animations of Computer-Controlled Virtual Characters Based on Probabilistic Human Reaction Modeling
Alsaggaf, W., Tsaramirsis, G., Al-Malki, N. A., Khan, F. Q., Almasry, M., Serafi, M. A., & Almarzuqi, A. A. (2020) · Applied Sciences · doi:10.3390/app10165636 · Cited by 5
Designing the catbus
Vishkaie, R. (2020) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3410530.3414379 · Cited by 5
Detecting Fake News Through Emotion Analysis
Mackey, A., Gauch, S., & Labille, K. (2021) · Cited by 5
Individual Differences in the Relationship Between Emotion and Performance in Command-and-Control Environments
Schmitz-Hübsch, A., Stasch, S.-M., & Fuchs, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77873-6_10 · Cited by 5
Affective State-Based Framework for e-Learning Systems
Rodríguez, J. A., Comas, J., & Binefa, X. (2021) · Frontiers in artificial intelligence and applications · doi:10.3233/faia210155 · Cited by 5
Attention emotion recognition via ECG signals
Mao, A., Du, Z., Lu, D., & Luo, J. (2021) · Quantitative Biology · doi:10.15302/j-qb-021-0267 · Cited by 5
Text as signal. A tutorial with case studies focusing on social media (Twitter)
Mayor, E., Bietti, L., & Canales-Rodríguez, E. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01917-1 · Cited by 7
Multimodal recognition of frustration during game-play with deep neural networks
de la Fuente, C., Castellanos, F. J., Valero-Mas, J. J., & Calvo-Zaragoza, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13762-7 · Cited by 6
What Does It Mean to Measure a Smile? Assigning numerical values to emotions
Arnelid, M., Harrison, K., & Johnson, E. (2022) · Valuation Studies · doi:10.3384/vs.2001-5992.2022.9.1.79-107 · Cited by 5
Electroencephalogram Emotion Recognition Based on Individual Frontal Asymmetry Hypothesis
Cao, G., Yang, L., & Ni, P. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995216 · Cited by 5
Context Based Vision Emotion Recognition in the Wild
Yuan, Y., Lu, F., Cheng, X., & Liu, Y. (2022) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea54703.2022.10005917 · Cited by 5
Representing affect information in word embeddings
Zhang, Y., Chen, W., Zhang, R., & Zhang, X. (2023) · Experiments in Linguistic Meaning · doi:10.3765/elm.2.5391 · Cited by 5
Künstliche Emotion – Zum ethischen Umgang mit Gefühlen zwischen Mensch und Technik
Manzeschke, A., & Assadi, G. (2023) · Ethik in der Medizin · doi:10.1007/s00481-023-00766-6 · Cited by 5
Multimodal Machine Learning in Prognostics and Health Management of Manufacturing Systems
Jose, S., Nguyen, K. T. P., & Medjaher, K. (2023) · Springer series in reliability engineering · doi:10.1007/978-3-031-30510-8_9 · Cited by 5
An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction
Wang, Z., Huang, R., Yan, Y., Luo, Z., Zhao, S., Wang, B., Jin, J., Xie, L., & Yin, E. (2023) · Bioengineering · doi:10.3390/bioengineering10101200 · Cited by 5
Apply EZStrobe to simulate the finishing work for reducing construction process waste
Pham, V. H. S., & Tien, P. V. (2024) · Scientific Reports · doi:10.1038/s41598-023-50442-4 · Cited by 5
HDEL: a hierarchical deep ensemble approach for text-based emotion detection
Vora, S. V., & Mehta, R. G. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19032-y · Cited by 5
Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals Using Artificial Intelligence Algorithms
Bamonte, M. F., Risk, M. R., & Herrero, V. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-61960-1_3 · Cited by 5
An Exploration of Machine Learning in Art and Design
Dursun, R. Z. A., & Bakan, U. (2024) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-4318-0.ch010 · Cited by 5
Identifying instant utility using psychophysiological indicators in a transport experiment with ecological validity
Henríquez-Jara, B., Guevara, C. Á., & Jimenez-Molina, A. (2025) · Transportation · doi:10.1007/s11116-025-10584-y · Cited by 5
Staged transfer learning for multi-label half-face emotion recognition
ElMaksoud, M. M. A., ElGohary, S. H., & Kandil, A. H. (2025) · Journal of Engineering and Applied Science · doi:10.1186/s44147-025-00615-x · Cited by 5
Intelligent Presentation Skills Trainer Analyses Body Movement
Nguyen, A.-T., Chen, W., & Matthias Rauterberg, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19222-2_27 · Cited by 5
Probabilistic prediction of student affect from hand gestures
Abbasi, A. R., Dailey, M. N., Afzulpurkar, N., & Uno, T. (2008) · Cited by 5
ViMood: Using social emotions to improve video indexing
Furini, M. (2015) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2015.7158073 · Cited by 5
EEG: A Way to Explore Learner’s Affect in Pervasive Learning Systems
Wan, J., Hu, B., & Li, X. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13067-0_15 · Cited by 5
Dynamic vs. Static Recognition of Facial Expressions
Raducanu, B., & Dornaika, F. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89617-3_2 · Cited by 5
Geometric Feature-Based Face Normalization for Facial Expression Recognition
Kim, D., Sohn, M., Kim, H., & Ryu, N. (2014) · International Conference on Artificial Intelligence, Modelling and Simulation (AIMS) · doi:10.1109/aims.2014.52 · Cited by 5
Towards a Supportive Technological Environment for Digital Art
Turner, G., & Edmonds, E. (2003) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 5
Finding Geometrical Associations Between Meaningful Objects in the Web: A Geostatistical Approach
Nocera, F. D., Capponi, C., & Ferlazzo, F. (2004) · Cited by 5
Frontal EEG Asymmetry and Affective States: A Multidimensional Directed Information Approach
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-13039-7_173 · Cited by 5
Combined User Physical, Physiological and Subjective Measures for Assessing User Cost
Lin, T., Imamiya, A., Hu, W., & Omata, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-71025-7_20 · Cited by 5
Sentiment and Behaviour Annotation in a Corpus of Dialogue Summaries
Alvares, A. R., Carvalho, A. M. B. R., Roman, N. T., & Piwek, P. (2020) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.3217/jucs-021-04-0561 · Cited by 5
Design of interaction in a virtual environment for post-stroke rehabilitation: A cognitive perspective
Zangiacomi, A., Redaelli, C., Valentini, F., & Bernardelli, G. (2014) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2014.7020439 · Cited by 5
Estimating Subjective Assessments Using a Simple Biosignal Sensor
Maki, Y., Sano, G., Kobashi, Y., Nakamura, T., Kanoh, M., & Yamada, K. (2012) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd.2012.45 · Cited by 5
Frame Semantics Evolutionary Model for Emotion Detection
Haggag, M. H. (2014) · Computer and Information Science · doi:10.5539/cis.v7n1p136 · Cited by 5
Caracolomobile: affect in computer systems
Fraga, T. M. D. A. (2012) · AI & Society · doi:10.1007/s00146-012-0392-4 · Cited by 5
Opinion Bias Detection with Social Preference Learning in Social Data
Kwon, A.-R., & Lee, K. (2013) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.2013070104 · Cited by 5
Determining mood using emotional features
Hashemian, M., Nikoukaran, A., Moradi, H., Mirian, M. S., & Tehrani‐Doost, M. (2014) · International Symposium on Telecommunications · doi:10.1109/istel.2014.7000740 · Cited by 5
Bringing the sport stadium atmosphere to remote fans
Centieiro, P. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502217 · Cited by 5
An Architecture for Emotional Agent
Hu, J., & Guan, C. (2006) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/iccias.2006.294181 · Cited by 5
Emotion Recognition in Real-world Conditions with Acoustic and Visual Features
Yuryevich, S. M., & Minker, W. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666279 · Cited by 5
Augmented emotion by superimposing depiction in comics
Sakurai, S., Narumi, T., Tanikawa, T., & Hirose, M. (2011) · International Conference on Advances in Computer Entertainment Technology (ACE) · doi:10.1145/2071423.2071506 · Cited by 5
Vocal emotion recognition in five languages of Assam using features based on MFCCs and Eigen Values of Autocorrelation Matrix in presence of babble noise
Kandali, A. B., Routray, A., & Basu, T. K. (2010) · National Conference on Communications · doi:10.1109/ncc.2010.5430205 · Cited by 5
Virtual worlds as settings for avatar-based innovation processes
Novak, N. M., Mladenow, A., & Strauss, C. (2014) · Journal of Service Science Research · doi:10.1007/s12927-014-0003-7 · Cited by 10
Biocybernetic adaptation and privacy
Böhle, K., Coenen, C., Decker, M., & Rader, M. (2012) · Innovation The European Journal of Social Science Research · doi:10.1080/13511610.2013.723402 · Cited by 5
The Rovereto Emotion and Cooperation Corpus: a new resource to investigate cooperation and emotions
Cavicchio, F., & Poesio, M. (2011) · Language Resources and Evaluation · doi:10.1007/s10579-011-9163-y · Cited by 5
Sentiment and sentimental agent identification based on sentimental sentence dictionary
Liu, D., Quan, C., Ren, F., & Chen, P. (2008) · International Conference on Natural Language Processing and Knowledge Engineering (NLPKE) · doi:10.1109/nlpke.2008.4906802 · Cited by 5
A Personality Based Emotion Model for Intelligent Virtual Agents
Liu, Z. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.805 · Cited by 5
Persona-Algo: Personalização dos Exercícios de Algoritmos auxiliados por um Agente Afetivo
Iepsen, E. F., Bercht, M., & Reategui, E. B. (2010) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · Cited by 5
Galvanic skin response-derived bookmarking of an audio stream
Pan, M. K. X. J., Chang, G. J.-S., Himmetoglu, G. H., Moon, A., Hazelton, T. W., MacLean, K. E., & Croft, E. A. (2011) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1979742.1979716 · Cited by 5
Behavior selection system based on emotional variations
Jitviriya, W., Koike, M., & Hayashi, E. (2015) · IEEE RO-MAN · doi:10.1109/roman.2015.7333588 · Cited by 5
An Emotional Intelligent E-learning System Based on Mobile Agent Technology
Wang, Z., Qiao, X., & Xie, Y. (2009) · International Conference on Computer Engineering and Technology · doi:10.1109/iccet.2009.17 · Cited by 5
Mobile Haptic Technology Development through Artistic Exploration
Cuartielles, D. J., Göransson, A. C., Olsson, T., & Stenslie, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32796-4_4 · Cited by 5
Enhancing Conversational Access to Information through a Socially Intelligent Agent
Carolis, B. N. D., Mazzotta, I., & Novielli, N. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14000-6_1 · Cited by 5
Analysis of Appropriate Timing for Information Notification Based on Indoor User's Location Transition
Kostov, V., Tajima, T., Naito, E., & Ozawa, J. (2006) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2006.8 · Cited by 5
CONSCIENCE OF BLOGS: VERIFYING CONTEXTUAL APPROPRIATENESS OF EMOTIONS BASED ON BLOG CONTENTS
Ptaszynski, M. E., Dybała, P., Shi, W., Rzepka, R., & Araki, K. (2009) · Cited by 5
Using affective signals as implicit indicators of information relevance and information processing strategies
González‐Ibáñez, R., & Shah, C. S. (2016) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2016.14505301050 · Cited by 5
The Research on Material Selection Algorithm Design with Improved OWA in Affective Regulation System Based on Human-computer Interaction
Yan, F. (2013) · Journal of Information and Computational Science · doi:10.12733/jics20102223 · Cited by 5
Multimedia Design and Situational Interest: A Look at Juxtaposition and Measurement
Dousay, T. A. (2014) · Educational media and technology yearbook · doi:10.1007/978-3-319-06314-0_5 · Cited by 5
The robot intermediary: Mechanical analogies and autism
Richardson, K. (2016) · Anthropology Today · doi:10.1111/1467-8322.12298 · Cited by 5

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
DOI: 10.7551/mitpress/1140.001.0001
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

Affective Computing

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks