Corpus ID: 48735108

Running head : AFFECTIVE COMPUTING MEASURES 1 The Affective Computing Approach to Affect Measurement

@inproceedings{DMello2017RunningH,
  title={Running head : AFFECTIVE COMPUTING MEASURES 1 The Affective Computing Approach to Affect Measurement},
  author={S. D'Mello},
  year={2017}
}
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… Expand

References

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  • Psychology, Computer Science
  • IEEE Transactions on Affective Computing
  • 2010
TLDR
This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations. Expand
Affect Elicitation for Affective Computing
The ability to reliably and ethically elicit affective states in the laboratory is critical when studying and developing systems that can detect, interpret, and adapt to human affect. Many methodsExpand
Smile When You Read This, Whether You Like It or Not: Conceptual Challenges to Affect Detection
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The survey by Calvo and D'Mello presents a useful overview of the progress of and issues in affect detection. They focus on emotion theories that are relevant to Affective Computing (AC) and suggestExpand
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'Affective computing' is a branch of computing concerned with the theory and construction of machines which can detect, respond to, and simulate human emotional states. It is an interdisciplinaryExpand
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TLDR
It is found that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projections and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral. Expand
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Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, theExpand
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Affective computing is bound up with ethics at multiple levels, from codes governing studies with human participants to debates about the proper relationship between ethical and emotional systemsExpand
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TLDR
Examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self- report and autonomic measures. Expand
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TLDR
Recent advances in dimensional and continuous affect modeling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities are explored. Expand
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TLDR
The concept and models of the affective music player worked in an ecologically valid setting, suggesting the feasibility of using physiological responses in real-life affective computing applications. Expand
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