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 4,701–4,800 of 7,380

Sentiment Analysis of News Titles
Loureiro, D., Marreiros, G., & Neves, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24769-9_1 · Cited by 3
Proposal for Non-Contact Analysis of Multimodal Inputs to Measure Stress Level in Serious Games
Bevilacqua, F., Backlund, P., & Engström, H. (2015) · International Conference on Games and Virtual Worlds for Serious Applications · doi:10.1109/vs-games.2015.7295783 · Cited by 3
The interpersonal effect of emotion in decision-making and social dilemmas
Gratch, J. S., & de Melo, C. M. (2012) · Cited by 3
A Novel Feature Selection Method for Affective Recognition Based on Pulse Signal
Chen, H., Liu, G., & Xie, X. (2011) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2011.36 · Cited by 3
On emotional aspects in Mission‐Oriented Opportunistic Networks
Misra, S., & Saha, B. K. (2013) · IET Networks · doi:10.1049/iet-net.2013.0080 · Cited by 3
How affective computing could complement and advance the quantified self
Keary, A., & Walsh, P. (2014) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2014.6999285 · Cited by 3
Exploring the health benefits of an emoting water cooler
Profita, H. P., Emil, H., Brinkman, D., & Smith, R. (2014) · ACM Conference Companion Publication on Designing Interactive Systems (DIS Companion) · doi:10.1145/2598784.2602797 · Cited by 3
Regression algorithm for emotion detection
Berthelon, F., & Sander, P. T. (2013) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2013.6719220 · Cited by 3
Analysis of Social Smile Sharing Using a Wearable Device that Captures Distal Electromyographic Signals
Gruebler, A., & Suzuki, K. (2012) · International Conference on Emerging Security Technologies · doi:10.1109/est.2012.38 · Cited by 3
Comparison of statistical methods for analysis of affective haptic expressions
Gaffary, Y., Eyharabide, V., Martin, J., & Ammi, M. (2012) · ACM Symposium on Applied Perception · doi:10.1145/2338676.2338710 · Cited by 3
Design and implementation of agent interaction mechanisms for emotionally intelligent tutoring systems
Petroviča, S., & Anohina-Naumeca, A. (2012) · Applied Computer Systems · doi:10.2478/v10312-012-0006-2 · Cited by 3
Emerging application areas and challenges of automatic face analysis
Gizatdinova, Y., Surakka, V., Haniff, S., Mäkinen, E., Raisamo, R., Iso-Tuisku, J., & Sand, A. (2013) · Continuum · doi:10.1080/10304312.2013.803308 · Cited by 3
Emotional Business Intelligence
Helfenstein, S., Kaikova, O., Khriyenko, O., & Terziyan, V. Y. (2014) · Conference on Human System Interactions · doi:10.1109/hsi.2014.6860441 · Cited by 3
Social navigation with the collective mobile mood monitoring system
Perttula, A., Koivisto, A. T., Mäkelä, R., Suominen, M., & Multisilta, J. (2011) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/2181037.2181057 · Cited by 3
Emotion-Oriented eCommerce Systems
Leon, S., & Nikov, A. (2015) · Cited by 3
A Fuzzy Logic Based Emotion Model for Virtual Human
Liu, Z., He, S., & Xiong, W. (2008) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2008.70 · Cited by 3
How Women Think Robots Perceive Them – as if Robots were Men
Pontier, M. A., & Hoorn, J. F. (2013) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0004253504960504 · Cited by 3
Colorful Natural Scenes Retrieval based on Affective Features Hierarchical Model
Kun, H. (2008) · International Symposium on Information Science and Engineering · doi:10.1109/isise.2008.142 · Cited by 3
Deducing User States of Engagement in Real Time by Using a Purpose Built Unobtrusive Physiological Measurement Device: An Empirical Study and HCI Design Challenges
Psaltis, A., Rizopoulos, C., Lekkas, Z., & Mourlas, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07527-3_6 · Cited by 3
Emotion Eliciting and Decision Making by Psychodynamic Appraisal Mechanism
Liú, J., & Ando, H. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-03202-8_18 · Cited by 3
Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music
Imbrasaitė, V., & Robinson, P. (2013) · Jyväskylä University Digital Archive (University of Jyväskylä) · Cited by 3
Spatio-temporal Dynamics of Images with Emotional Bivalence
Murcia, M. D. G., López-Gordo, M. A., Ortiz, M. J., Ferrández, J. M., & Fernández, E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18914-7_21 · Cited by 3
AnEvaluation of Computational Modeling in Cognitive Science
Boden, M. A. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816772.030 · Cited by 3
Seeing emotions: a review of micro and subtle emotion expression training
Poole, E. (2016) · Cultural Studies of Science Education · doi:10.1007/s11422-015-9698-0 · Cited by 2
Human Factors in Web Adaptation and Personalization
Germanakos, P., & Belk, M. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-28050-9_2 · Cited by 3
Speech Matters – Psychological Aspects of Artificial versus Anthropomorphic System Voices in User-Companion Interaction
Ferchow, S., Haase, M., Krüger, J., Vogel, M., Wahl, M., & Frommer, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-40406-6_30 · Cited by 3
Analysis of student's context in e-Learning
Gonçalves, S., Carneiro, D. R., Alfonso‐Cendón, J., Fdez‐Riverola, F., & Nováis, P. (2014) · International Symposium on Computers in Education · doi:10.1109/siie.2014.7017726 · Cited by 3
Emotional Platform for Marketing Research
Navarro, A., Delevoye, C., & Oyarzun, D. (2016) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-39345-2_43 · Cited by 3
A Model of Emotion for Empathic Communication
Hieida, C., & Nagai, T. (2017) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3029798.3038299 · Cited by 3
Brain Tagging: A BCI and HCI Tagging System to Evaluate the Learning Contents
Shen, Y. T., Lu, P. W., & Chen, X. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58509-3_5 · Cited by 3
Toward an “Equal-Footing” Human-Robot Interaction for Fully Autonomous Vehicles
Amanatidis, T., Langdon, P., & Clarkson, P. J. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-60384-1_30 · Cited by 3
Social Presence and E-Learning.
Strongman, L., & Kobeleva, P. (2012) · Cited by 3
Toward a Fuzzy Approach for Emotion Generation Dynamics Based on OCC Emotion Model
Soleimani, A., & Kobti, Z. (2014) · IAENG international journal of computer science · Cited by 3
MULTI-LEVEL ADAPTATION OF AN EDUCATIONAL GAME TO INDIVIDUAL STUDENT’S GAMEPLAY, KNOWLEDGE AND EMOTIONS
Petroviča, S. (2017) · EDULEARN proceedings · doi:10.21125/edulearn.2017.1462 · Cited by 3
A methodological approach — Working emotions and learning using facial expressions
Paxiúba, C. M. C., & Lima, C. P. (2018) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2018.8399189 · Cited by 3
Extracting Interpersonal Stance from Vocal Signals
Formolo, D., & Bosse, T. (2018) · doi:10.1145/3279972.3279974 · Cited by 3
Beep Beep: Building Trust with Sound
Schmidmaier, M., Hußmann, H., & Runge, D. M. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3382848 · Cited by 3
Creating Emotions Through Digital Media Art
Gomes, P. V., Sá, V. J., Marques, A. J., Donga, J., Correia, A., & Pereira, J. (2020) · Advances in media, entertainment and the arts (AMEA) book series · doi:10.4018/978-1-7998-3669-8.ch007 · Cited by 3
Affecting reality
Ng, C. K. M. (2019) · A Peer-Reviewed Journal About · doi:10.7146/aprja.v8i1.115418 · Cited by 3
A Social Condition-Enhanced Network for Recognizing Power Distance Using Expressive Prosody and Intrinsic Brain Connectivity
Tsai, F.-S., Chang, W., & Lee, C.-C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3075091 · Cited by 3
A survey on the development of intelligent robots in speech emotion recognition
Gao, Q., Ning, H., & Du, B. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498858 · Cited by 3
Enabling Speech Emotional Intelligence as a Service in Homecare Platforms
Stavrianos, P., Pavlopoulos, A., & Maglogiannis, I. G. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-77746-3_9 · Cited by 3
De las emociones naturales a la emocionalidad artificial
Fernández, J. M. B. (2021) · Cuadernos salmantinos de filosofía · doi:10.36576/summa.144495 · Cited by 3
Real-time Ubiquitous Pain Recognition
Tynes, I., & Canavan, S. J. (2021) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw52867.2021.9666289 · Cited by 3
Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention
Raju, J., Gaus, Y. F. A., & Breckon, T. P. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00115 · Cited by 3
Collected Papers
Turkia, M. (2009) · Springer eBooks · doi:10.1007/978-3-211-09451-8_11 · Cited by 3
Collectively playable wearable music: Practice-situated approaches to participatory relational inquiry
Thorn, S. D., & Willcox, H. (2022) · Wearable Technologies · doi:10.1017/wtc.2021.19 · Cited by 3
Empowering Well-Being Through Conversational Coaching for Active and Healthy Ageing
McTear, M., Jokinen, K., Dubey, M., Chollet, G., Boudy, J., Lohr, C., Roelen, S. D., Mössing, W. A., & Wieching, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09593-1_21 · Cited by 3
Humanoids at the Helm of the Nursing Profession in Elderly Care: Critical Review
Dankar, M. J., & Badr, N. G. (2022) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-10902-7_1 · Cited by 3
Social Distancing and Social Biosensing: Intersubjectivity from Afar
Curran, M. T., & Chuang, J. C.-I. (2022) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-022-09428-5 · Cited by 4
Empathic Smart Conversational Agent for Enhanced Recovery from Abdominal Surgery at Home
Díaz, F. D., Pedrosa, I., Quirós, P., & Díaz, P. Á. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-19679-9_51 · Cited by 3
In-process feedback by detecting deadlock based on EEG data in exercise of learning by problem- posing and its evaluation
Yamamoto, S., Tobe, Y., Tawatsuji, Y., & Hirashima, T. (2022) · Research and Practice in Technology Enhanced Learning · doi:10.58459/rptel.2023.18028 · Cited by 3
Balancing Game Elements, Learning, and Emotions in Game Design
Natucci, G. C., & Borges, M. A. F. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-27639-2_5 · Cited by 4
Digitale Hate Speech
Jaki, S., & Steiger, S. (2023) · Springer eBooks · doi:10.1007/978-3-662-65964-9 · Cited by 7
Fuzzy Conversational Character Computing
Hundertmark, S., Portmann, E., & Hafner, N. (2023) · Informatik-Spektrum · doi:10.1007/s00287-023-01538-8 · Cited by 4
A Dataset of Gaze and Mouse Patterns in the Context of Facial Expression Recognition
Bruckert, A., Lévêque, L., Silva, M. P. D., & Callet, P. L. (2023) · ACM International Conference on Interactive Media Experiences · doi:10.1145/3573381.3596153 · Cited by 3
Eliciting and Annotating Emotion in Virtual Spaces
Xylakis, E., Najm, A., Liapis, A., Yannakakis, G. N., & Michael‐Grigoriou, D. (2023) · eCAADe proceedings · doi:10.52842/conf.ecaade.2023.2.841 · Cited by 3
Expanding the Role of Affective Phenomena in Multimodal Interaction Research
Mathur, L., Matarić, M. J., & Morency, L. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3614171 · Cited by 3
ASAR Dataset and Computational Model for Affective State Recognition During ARAT Assessment for Upper Extremity Stroke Survivors
Ahmed, T., Rikakis, T., Kelliher, A., & Soleymani, M. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3610661.3617154 · Cited by 3
Emotion-based Dynamic Difficulty Adjustment in Video Games
Kutt, K., Ściga, Ł., & Nalepa, G. J. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302578 · Cited by 3
Academic Emotion Prediction in Online Learning Utilizing Deep Learning Approach
Rathi, S. R., Deshpande, Y. D., Chavhan, P. G., & More, P. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179187123040010 · Cited by 3
Emotion-performance relationship in safety-critical human-machine systems
Schmitz-Hübsch, A., Becker, R., & Wirzberger, M. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100364 · Cited by 3
Towards Facial Expression Recognition in Immersive Virtual Reality with EmojiRain
Ortmann, T. (2023) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw59127.2023.10388094 · Cited by 3
Pain Management and Conditioning through Virtual Reality and Affective Computing
Bugeja, A., & Dingli, A. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385256 · Cited by 3
Integrating Artificial Intelligence with Human Psychology
Abrar, S. M. A. M. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-15250 · Cited by 3
Emotion Identification Based on EEG Rhythms Separated using Improved Eigenvalue Decomposition of Hankel Matrix
Nalwaya, A., Singh, V. K., & Pachori, R. B. (2023) · doi:10.1109/icsc60394.2023.10441313 · Cited by 3
AI for Musical Discovery
Singh, N., Mishra, M., & Machover, T. (2024) · doi:10.21428/e4baedd9.8fa181e9 · Cited by 4
FPGA Implementation of CNN-LSTM Classifier in Speech Emotion Recognition System
Gao, Z., Xiao, W., Zhou, W., & Yang, Z. (2023) · International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · doi:10.1109/hdis60872.2023.10499604 · Cited by 4
Trusted emotion recognition based on multiple signals captured from video and its application in intelligent education
Zhang, J., Fei, C., Zheng, Y., Zheng, K., Mazhar, S., & Li, Y. (2024) · Electronic Research Archive · doi:10.3934/era.2024161 · Cited by 4
Feature Alignment and Reconstruction Constraints for Multimodal Sentiment Analysis
Zhu, Q., Lan, T., Liang, J. P., Xiang, D., & He, H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650976 · Cited by 3
The Role of Affective Computing in Social Justice: Harnessing Equity and Inclusion
Chaube, N., Kerketta, R., Sharma, S., & Shinde, A. (2024) · The Springer series in applied machine learning · doi:10.1007/978-3-031-63821-3_4 · Cited by 3
From Signals to Emotion: Affective State Classification through Valence and Arousal
Kacimi, Y., & Adda, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.121 · Cited by 3
Partial Label Learning for Emotion Recognition From EEG
Zhang, G., & Etemad, A. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3562027 · Cited by 9
Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers
Barthet, M., Branco, D., Gallotta, R., Khalifa, A., & Yannakakis, G. N. (2024) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii63134.2024.00034 · Cited by 4
Mobile Game Evaluation Method Based on Data Mining of Affective Time Series
Ochab, J. K., Wȩgrzyn, P., Witaszczyk, P., Drążyk, D., & Nalepa, G. J. (2025) · Sensors · doi:10.3390/s25092756 · Cited by 6
Design and Evaluation of a Sound-Driven Robot Quiz System with Fair First-Responder Detection and Gamified Multimodal Feedback
Tutul, R., & Niels Pinkwart, C. (2025) · Robotics · doi:10.3390/robotics14090123 · Cited by 5
Emotion, Language, and Trust: Rethinking E-Commerce Support with Generative AI and Ethical Messaging
Kole, D., Kapoor, S., Soni, J. A., Sahoo, I., Kumar, U., Bidkar, D. M., Dontireddy, S. R. R., & Aggarwal, A. (2025) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv66517.2025.11203627 · Cited by 5
A Conceptual Framework for Using the Affective Computing Techniques to Evaluate the Outcome of Digital Game-Based Learning
Wu, C., Tzeng, Y.-L., & Huang, R. Y. M. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-94-007-7262-5_22 · Cited by 2
Online Mood Induction
Loiacono, E. T., Taylor, N. J., & Djamasbi, S. (2005) · Digital WPI · Cited by 3
A MOBILE INTELLIGENT SYNTHETIC CHARACTER WITH NATURAL BEHAVIOR GENERATION
Yoon, J.-W., & Cho, S. (2010) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0002723003150318 · Cited by 3
Heart Rate Monitoring as an Easy Way to Increase Engagement in Human-Agent Interaction
Frey, J. (2015) · International Conference on Physiological Computing Systems (PhyCS) · doi:10.5220/0005226101290136 · Cited by 3
Value‐centred interaction design methods
Knight, J. (2008) · Journal of Information Communication and Ethics in Society · doi:10.1108/14779960810921132 · Cited by 3
Expanding the Magic Circle in Pervasive Casual Play
Reis, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33542-6_60 · Cited by 3
A framework for designing emotional agents as tutoring entities
Marin, B. F., Hunger, A., & Werner, S. (2006) · IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) · doi:10.1109/saint.2006.2 · Cited by 3
Emotional speaker recognition in simulated and spontaneous context
Mansour, A., & Lachiri, Z. (2016) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip.2016.7523187 · Cited by 3
Simulation of Human Opinions about Calligraphy Aesthetic
Pérez, A. V., Cermeño, E., & Siguenza, J. A. (2014) · International Conference on Artificial Intelligence, Modelling and Simulation (AIMS) · doi:10.1109/aims.2014.34 · Cited by 3
An Analysis of Previous Information Systems Research on Emotions.
Mian, S. Q., & B, H. O.-K. A. (2016) · Cited by 3
The Willful Marionette
Grace, K. S., Grace, S., Maher, M. L., Mahzoon, M. J., Lee, L., LoCurto, L., & Outcault, B. (2017) · Creativity and Cognition · doi:10.1145/3059454.3059475 · Cited by 5
EmIoT: Giving Emotional Intelligence to the Internet of Things
Nitti, M., Pilloni, V., & Atzori, L. (2018) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2018.8463412 · Cited by 3
The Multimodal Dataset of Negative Affect and Aggression
Lefter, I., & Fitrianie, S. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3243013 · Cited by 3
Towards Truly Affective AAL Systems
Pudāne, M., Petroviča, S., Lavendelis, E., & Ekenel, H. K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10752-9_7 · Cited by 3
Significant Patterns of APPRAISAL in Online Debates
Almutairi, B. A. A. (2019) · International Journal of Linguistics · doi:10.5296/ijl.v11i4.15040 · Cited by 4
Technologies of the Musical Selfie
Gopinath, S., & Stanyek, J. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316676639.009 · Cited by 4
Pyramid Memory Block and Timestep Attention for Speech Emotion Recognition
Cao, M. Y., Yang, C. H., Zhou, F., & Yin, X.-C. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3140 · Cited by 3
Brain 2 Communicate: EEG-based Affect Recognition to Augment Virtual Social Interactions
Roth, D., Westermeier, F., Brübach, L., Feigl, T., Schell, C., & Latoschik, M. E. (2019) · Gesellschaft für Informatik (GI) · doi:10.18420/muc2019-ws-571 · Cited by 3
“Be Social”—Embodied Human-Robot Musical Interactions
Weinberg, G., Bretan, M., Hoffman, G., & Driscoll, S. (2020) · Automation, collaboration, and e-services · doi:10.1007/978-3-030-38930-7_5 · Cited by 4
Digital Empathy: Kombinierte Erfassung über Affective Computing und Eye Tracking
Hahn, A., Klug, K., & Riedmüller, F. (2020) · Digitale Welt · doi:10.1007/s42354-020-0278-4 · Cited by 3
Emotion Detection Based on Smartphone Using User Memory Tasks and Videos
Simonazzi, N., Salotti, J., Dubois, C., & Séminel, D. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55307-4_37 · Cited by 3
Safety perception and pedestrian dynamics: Experimental results towards affective agents modeling
Gasparini, F., Giltri, M., & Bandini, S. (2021) · AI Communications · doi:10.3233/aic-201576 · Cited by 3
Development of Unified Neuro-Affective Classification Tool (UNACT)
Hossain, F., Yaacob, H. S., & Nordin, A. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1077/1/012031 · Cited by 3

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