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Journal articles on the topic 'Affective Computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

Hudlicka, Eva, Psychometrix Associates, Michael McNeese, et al. "Affective Computing." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 43, no. 3 (1999): 275–78. http://dx.doi.org/10.1177/154193129904300331.

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2

Armony, Jorge L. "Affective Computing." Trends in Cognitive Sciences 2, no. 7 (1998): 270. http://dx.doi.org/10.1016/s1364-6613(98)01190-5.

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3

Lisetti, C. L. "Affective computing." Pattern Analysis and Applications 1, no. 1 (1998): 71–73. http://dx.doi.org/10.1007/bf01238028.

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4

Thompson, Nik, and Tanya Jane McGill. "Affective Stack — A Model for Affective Computing Application Development." Journal of Software 10, no. 8 (2015): 919–30. http://dx.doi.org/10.17706//jsw.10.8.919-930.

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5

Thompson, Nik, and Tanya Jane McGill. "Affective Stack — A Model for Affective Computing Application Development." Journal of Software 10, no. 8 (2015): 919–30. http://dx.doi.org/10.17706/jsw.10.8.919-930.

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6

Noyes, Jan. "Review: Affective Computing." Perception 27, no. 5 (1998): 631–32. http://dx.doi.org/10.1068/p270631.

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7

Picard, Rosalind W. "Affective computing: challenges." International Journal of Human-Computer Studies 59, no. 1-2 (2003): 55–64. http://dx.doi.org/10.1016/s1071-5819(03)00052-1.

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8

Jin, Jing. "Symposium Title: Affective Computing and Affective Neuroscience." International Journal of Psychophysiology 168 (October 2021): S53. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.161.

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9

Khanna, Rahul, Nicole Robinson, Meaghan O’Donnell, Harris Eyre, and Erin Smith. "Affective Computing in Psychotherapy." Advances in Psychiatry and Behavioral Health 2, no. 1 (2022): 95–105. http://dx.doi.org/10.1016/j.ypsc.2022.05.006.

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10

KALIOUBY, R. E., R. PICARD, and S. BARON-COHEN. "Affective Computing and Autism." Annals of the New York Academy of Sciences 1093, no. 1 (2006): 228–48. http://dx.doi.org/10.1196/annals.1382.016.

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11

Maier, Marco, Lilian Schröder, Michael Bartl, and Regina Burgmayr. "Was ist Affective Computing?" Digitale Welt 3, no. 4 (2019): 72–74. http://dx.doi.org/10.1007/s42354-019-0216-5.

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12

Andre, Elisabeth. "Editorial: Transactions on Affective Computing – Affective Computing in the Times of Pandemics." IEEE Transactions on Affective Computing 12, no. 1 (2021): 1. http://dx.doi.org/10.1109/taffc.2021.3059491.

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13

Thompson, Nik, and Tanya Jane McGill. "Affective Tutoring Systems." International Journal of Information and Communication Technology Education 8, no. 4 (2012): 75–89. http://dx.doi.org/10.4018/jicte.2012100107.

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This paper introduces the field of affective computing, and the benefits that can be realized by enhancing e-learning applications with the ability to detect and respond to emotions experienced by the learner. Affective computing has potential benefits for all areas of computing where the computer replaces or mediates face to face communication. The particular relevance of affective computing to e-learning, due to the complex interplay between emotions and the learning process, is considered along with the need for new theories of learning that incorporate affect. Some of the potential means for inferring users’ affective state are also reviewed. These can be broadly categorized into methods that involve the user’s input, and methods that acquire the information independent of any user input. This latter category is of particular interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions or physiological state. The paper concludes with a review of prominent affective tutoring systems and promotes future directions for e-learning that capitalize on the strengths of affective computing.

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14

Kumar, Saurabh. "Deep learning based affective computing." Journal of Enterprise Information Management 34, no. 5 (2021): 1551–75. http://dx.doi.org/10.1108/jeim-12-2020-0536.

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PurposeDecision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.Design/methodology/approachThe present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.FindingsThe result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.Originality/valueThe study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

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15

Assabumrungrat, Rawin, Soravitt Sangnark, Thananya Charoenpattarawut, et al. "Ubiquitous Affective Computing: A Review." IEEE Sensors Journal 22, no. 3 (2022): 1867–81. http://dx.doi.org/10.1109/jsen.2021.3138269.

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16

Fong, Bernard, and Joyce Westerink. "Affective Computing in Consumer Electronics." IEEE Transactions on Affective Computing 3, no. 2 (2012): 129–31. http://dx.doi.org/10.1109/t-affc.2012.20.

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17

Clear, Tony. "Affective dimensions of computing education." ACM Inroads 2, no. 4 (2011): 12–13. http://dx.doi.org/10.1145/2038876.2038878.

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18

Hollnagel, Erik. "Is affective computing an oxymoron?" International Journal of Human-Computer Studies 59, no. 1-2 (2003): 65–70. http://dx.doi.org/10.1016/s1071-5819(03)00053-3.

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19

Xie, Haoran, Tak-Lam Wong, Fu Lee Wang, Raymond Wong, Xiaohui Tao, and Ran Wang. "Editorial: Affective and sentimental computing." International Journal of Machine Learning and Cybernetics 10, no. 8 (2019): 2043–44. http://dx.doi.org/10.1007/s13042-019-00977-8.

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20

Cambria, Erik. "Affective Computing and Sentiment Analysis." IEEE Intelligent Systems 31, no. 2 (2016): 102–7. http://dx.doi.org/10.1109/mis.2016.31.

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21

Muller, Michael. "Multiple paradigms in affective computing." Interacting with Computers 16, no. 4 (2004): 759–68. http://dx.doi.org/10.1016/j.intcom.2004.06.005.

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22

Nissan, Ephraim. "Rosalind W. Picard,Affective Computing." Pragmatics and Cognition 7, no. 1 (1999): 226–39. http://dx.doi.org/10.1075/pc.7.1.14nis.

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23

Schuller, Björn W., and Matti Pietikäinen. "Affective Computing [Scanning the Issue]." Proceedings of the IEEE 111, no. 10 (2023): 1139–41. http://dx.doi.org/10.1109/jproc.2023.3318028.

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24

Johnson, Richard. "What Next for Affective Computing?" New Electronics 51, no. 19 (2019): 30–31. http://dx.doi.org/10.12968/s0047-9624(22)61462-7.

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25

김미혜. "Exploring the Connection between Wearable Computing and Affective Computing - Design Guidelines for Affective Wearable Systems -." Journal of Digital Design 9, no. 4 (2009): 411–20. http://dx.doi.org/10.17280/jdd.2009.9.4.040.

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26

Picard, Rosalind W. "Affective Computing: From Laughter to IEEE." IEEE Transactions on Affective Computing 1, no. 1 (2010): 11–17. http://dx.doi.org/10.1109/t-affc.2010.10.

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27

Schwark, Jeremy D. "Toward a Taxonomy of Affective Computing." International Journal of Human-Computer Interaction 31, no. 11 (2015): 761–68. http://dx.doi.org/10.1080/10447318.2015.1064638.

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28

Hudlicka, Eva. "Response: Is affective computing an oxymoron?" International Journal of Human-Computer Studies 59, no. 1-2 (2003): 71–75. http://dx.doi.org/10.1016/s1071-5819(03)00080-6.

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29

McNeese, Michael D. "Response: is affective computing an oxymoron?" International Journal of Human-Computer Studies 59, no. 1-2 (2003): 77–80. http://dx.doi.org/10.1016/s1071-5819(03)00081-8.

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30

Lee, William, and Michael D. Norman. "Affective Computing as Complex Systems Science." Procedia Computer Science 95 (2016): 18–23. http://dx.doi.org/10.1016/j.procs.2016.09.288.

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31

Politou, Eugenia, Efthimios Alepis, and Constantinos Patsakis. "A survey on mobile affective computing." Computer Science Review 25 (August 2017): 79–100. http://dx.doi.org/10.1016/j.cosrev.2017.07.002.

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32

Becker-Asano, Christian. "Affective Computing Combined with Android Science." KI - Künstliche Intelligenz 25, no. 3 (2011): 245–50. http://dx.doi.org/10.1007/s13218-011-0116-9.

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33

Pitt, Jeremy. "Design Contractualism for Pervasive/Affective Computing." IEEE Technology and Society Magazine 31, no. 4 (2012): 22–29. http://dx.doi.org/10.1109/mts.2012.2225458.

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34

Ward, R. D., and P. H. Marsden. "Affective computing: problems, reactions and intentions." Interacting with Computers 16, no. 4 (2004): 707–13. http://dx.doi.org/10.1016/j.intcom.2004.06.002.

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35

Richardson, Sharon. "Affective computing in the modern workplace." Business Information Review 37, no. 2 (2020): 78–85. http://dx.doi.org/10.1177/0266382120930866.

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Affective computing refers to a class of computer-based solutions that analyse data about human behaviour, such as facial expressions, gestures, and language, for its emotional information. The term was first coined 25 years ago when the ability for computers to perform basic sensorimotor tasks such as object detection in images was in its infancy. This article revisits the subject and considers how it is being applied in real-world applications today. We look at research from cognitive science informing our understanding of emotions and how computing capabilities have advanced in recent years to produce cognitive algorithms capable of detecting human attention, emotion and health. Affective computing offers the potential to revolutionise how we incorporate emotion as information in communications and decision systems. However, much of the underlying research that forms the foundations for emotion detection is being challenged, raising concerns about the ethics, trustworthiness and viability of such platforms. This article presents a critique of the technologies and appraises their suitability for real-world applications as part of a modern workplace.

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36

Nalepa, Grzegorz J., José Palma, and María Trinidad Herrero. "Affective computing in ambient intelligence systems." Future Generation Computer Systems 92 (March 2019): 454–57. http://dx.doi.org/10.1016/j.future.2018.11.016.

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37

Gratch, Jonathan, Gretchen Greene, Rosalind Picard, Lachlan Urquhart, and Michel Valstar. "Guest Editorial: Ethics in Affective Computing." IEEE Transactions on Affective Computing 15, no. 1 (2024): 1–3. http://dx.doi.org/10.1109/taffc.2023.3322918.

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38

Hu, Xin, Jingjing Chen, Fei Wang, and Dan Zhang. "Ten challenges for EEG-based affective computing." Brain Science Advances 5, no. 1 (2019): 1–20. http://dx.doi.org/10.1177/2096595819896200.

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The emerging field of affective computing focuses on enhancing computers’ ability to understand and appropriately respond to people’s affective states in human-computer interactions, and has revealed significant potential for a wide spectrum of applications. Recently, the electroencephalography (EEG) based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application. The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology, psychology, and neuroscience. On the theoretical side, we suggest that researchers should be well aware of the limitations of the commonly used emotion models, and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms. On the practical side, we propose several operational recommendations for the challenges about data collection, feature extraction, model implementation, online system design, as well as the potential ethical issues. The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.

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39

Weinel, Jonathan, Stuart Cunningham, Darryl Griffiths, Shaun Roberts, and Richard Picking. "Affective Audio." Leonardo Music Journal 24 (December 2014): 17–20. http://dx.doi.org/10.1162/lmj_a_00189.

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The authors discuss their interdisciplinary research, which investigates the use of affective computing technologies in the context of music, audiovisual artworks and video games. One current project involves the expansion of mobile sound walk apps through incorporating environmental and emotional factors, forming new sonic landscapes. What type of music could reflect driving through a hot desert landscape at midday or walking through a snowy cityscape at dawn? Through a discussion of their collective work in this area, the authors aim to elicit a vision of the computer-based musical experiences of the future.

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40

Oh, Jee-Sun, Dan-Bee Back, and Duk-Hee Lee. "Analytical Research on Knowledge Production, Knowledge Structure, and Networking in Affective Computing." Korean Society for Emotion and Sensibility 23, no. 4 (2020): 61–72. http://dx.doi.org/10.14695/kjsos.2020.23.4.61.

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41

Abdullahi, Bashir Eseyin, Emeka Ogbuju, Taiwo Abiodun, and Francisca Oladipo. "Techniques for facial affective computing: A review." Ukrainian Journal of Educational Studies and Information Technology 11, no. 3 (2023): 211–26. http://dx.doi.org/10.32919/uesit.2023.03.05.

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Facial affective computing has gained popularity and become a progressive research area, as it plays a key role in human-computer interaction. However, many researchers lack the right technique to carry out a reliable facial affective computing effectively. To address this issue, we presented a review of the state-of-the-art artificial intelligence techniques that are being used for facial affective computing. Three research questions were answered by studying and analysing related papers collected from some well-established scientific databases based on some exclusion and inclusion criteria. The result presented the common artificial intelligence approaches for face detection, face recognition and emotion detection. The paper finds out that the haar-cascade algorithm has outperformed all the algorithms that have been used for face detection, the Convolutional Neural Network (CNN) based algorithms have performed best in face recognition, and the neural network algorithm with multiple layers has the best performance in emotion detection. A limitation of this research is the access to some research papers, as some documents require a high subscription cost. Practice implication: The paper provides a comprehensive and unbiased analysis of existing literature, identifying knowledge gaps and future research direction and supports evidence-based decision-making. We considered articles and conference papers from well-established databases. The method presents a novel scope for facial affective computing and provides decision support for researchers when selecting plans for facial affective computing.

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42

Cui, Xue, and Yiting Nan. "Improving Affective Computing Based on Semantic Analysis." Journal of Engineering Science and Technology Review 12, no. 5 (2019): 36–42. http://dx.doi.org/10.25103/jestr.125.05.

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43

Kim, Seong-Kyu (Steve). "Affective Computing Among Individuals in Deep Learning." Journal of Multimedia Information System 7, no. 2 (2020): 115–24. http://dx.doi.org/10.33851/jmis.2020.7.2.115.

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44

Hu, Xin, Jingjing Chen, Fei Wang, and Dan Zhang. "Ten challenges for EEG-based affective computing." Brain Science Advances 5, no. 1 (2019): 1–20. http://dx.doi.org/10.26599/bsa.2019.9050005.

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45

Yoon, Hyun Joong, and Seong Youb Chung. "Fuzzy Emotion Model for Affective Computing Agents." Journal of Society of Korea Industrial and Systems Engineering 37, no. 4 (2014): 1–11. http://dx.doi.org/10.11627/jkise.2014.37.4.01.

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46

Qi, Luo. "Affective Computing Model in E-Learning System." Advanced Science Letters 5, no. 1 (2012): 390–93. http://dx.doi.org/10.1166/asl.2012.3164.

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47

Calvo, Rafael A. "Latent and Emergent Models in Affective Computing." Emotion Review 2, no. 3 (2010): 288–89. http://dx.doi.org/10.1177/1754073910368735.

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48

D’Mello, Sidney, Arvid Kappas, and Jonathan Gratch. "The Affective Computing Approach to Affect Measurement." Emotion Review 10, no. 2 (2017): 174–83. http://dx.doi.org/10.1177/1754073917696583.

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Affective computing (AC) adopts a computational approach to study affect. We highlight the AC approach towards automated affect measures that jointly model machine-readable physiological/behavioral signals with affect estimates as reported by humans or experimentally elicited. We describe the conceptual and computational foundations of the approach followed by two case studies: one on discrimination between genuine and faked expressions of pain in the lab, and the second on measuring nonbasic affect in the wild. We discuss applications of the measures, analyze measurement accuracy and generalizability, and highlight advances afforded by computational tipping points, such as big data, wearable sensing, crowdsourcing, and deep learning. We conclude by advocating for increasing synergies between AC and affective science and offer suggestions toward that direction.

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49

Bozhkov, Lachezar, Petia Georgieva, Isabel Santos, Ana Pereira, and Carlos Silva. "EEG-based Subject Independent Affective Computing Models." Procedia Computer Science 53 (2015): 375–82. http://dx.doi.org/10.1016/j.procs.2015.07.314.

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50

Shams, Wafaa Khazaal, Abdul Wahab, and Imad Fakhri. "Affective Computing Model Using Source-temporal Domain." Procedia - Social and Behavioral Sciences 97 (November 2013): 54–62. http://dx.doi.org/10.1016/j.sbspro.2013.10.204.

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