Multimodal Affective Computing
Multimodal Affective Computing is a computational framework within affective computing that integrates multiple modalities—such as facial expressions, vocal prosody, physiological signals, and behavioral cues—to recognize, interpret, and respond to learners’ emotional states in educational contexts with greater accuracy and robustness than unimodal approaches.
533
Publications
36.4K
Citations
1.8K
Authors
597
Institutions
Publications per year
2017–2026
363
533
Affective Computing and Sentiment Analysis
Erik Cambria · IEEE Intelligent Systems · 2016 · 1.2K citations
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
Wei‐Long Zheng, Wei Liu, Bao‐Liang Lu et al. · IEEE Transactions on Cybernetics · 2018 · 1.2K citations
Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection
Philip Schmidt, Attila Reiss, Robert Duerichen et al. · 2018 · 1.2K citations
1–5 of 533
1.8K
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597

Imperial College London
London, United Kingdom
18 h-index
38 publications
6.2K citations
University of Augsburg
Augsburg, Germany
18 h-index
68 publications
3.2K citations
Nanyang Technological University
Singapore, Singapore
15 h-index
23 publications
2.3K citations

Shanghai Jiao Tong University
Shanghai, China
12 h-index
34 publications
7.3K citations
Universität Ulm
Ulm, Germany
11 h-index
30 publications
1.3K citations

University of Passau
Passau, Germany
10 h-index
20 publications
1.4K citations
University of Geneva
Geneva, Switzerland
10 h-index
24 publications
5.2K citations
University of Southern California
Los Angeles, United States
10 h-index
26 publications
1.8K citations
1–8 of 597
TAC · 25 publications · 4.6K citations
22
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12 publications · 480 citations
11
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11 publications · 465 citations
11
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Repository · 16 publications · 366 citations
11
h-index
10 publications · 513 citations
10
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1–5