Affective computing: challenges
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Automatic human emotion classification remains very challenging due to the idiosyncrasy and variability of human emotional expressions.
“automatic human emotion classification remains very challenging due to the idiosyncrasy and variability of human emotional expressions [11] .”
From Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection (2019) · cites this paper as [11]
Many accessible physiological signals may be non-differentiable in emotion detection.
“Many accessible physiological signals may be non-differentiable in emotion detection [11] .”
From Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection (2019) · cites this paper as [11]
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Fundamentals of physiological computing
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A review of verbal and non-verbal human–robot interactive communication
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- Authors
- Picard, R. W.
- Affiliations
- MIT Media Lab (US)
- Corresponding author
- Picard, Rosalind W.
- ORCID
- Picard, Rosalind W.
- Venue
- International Journal of Human-Computer Studies
- Publisher
- Elsevier BV
- Published
- 2003-07
- Volume
- 59
- Issue
- 1-2
- Pages
- 55-64
- ISSN
- 1071-5819 (ISSN-L)
- OpenAlex
- W2018543616
- Cited by
- 978
- References
- 16
- Citations by year
- 2026: 32 · 2025: 38 · 2024: 60 · 2023: 65 · 2022: 82 · 2021: 55 · 2020: 71 · 2019: 41 · 2018: 54 · 2017: 53 · 2016: 56 · 2015: 58 · 2014: 47 · 2013: 58 · 2012: 35
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Studies (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- affective computing, emotional expression, emotional communication, emotional responses, human-computer interaction, research challenges
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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.
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