research paper on affective computing

A Comprehensive Survey on Affective Computing: Challenges, Trends, Applications, and Future Directions

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Title: a comprehensive survey on affective computing; challenges, trends, applications, and future directions.

Abstract: As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact together. This paper discusses the significance of affective computing, as well as its ideas, conceptions, methods, and outcomes. By using approaches of ML and XR, we survey and discuss recent methodologies in affective computing. We survey the state-of-the-art approaches along with current affective data resources. Further, we discuss various applications where affective computing has a significant impact, which will aid future scholars in gaining a better understanding of its significance and practical relevance.

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Affective Computing: An Introduction to the Detection, Measurement, and Current Applications

• First Online: 03 October 2021

Cite this chapter

• Geoffrey Gaudi 8 ,
• Bill Kapralos 8 , 9 ,
• K. C. Collins 10 &
• Alvaro Quevedo 8  

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 22))

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1 Citations

Affective computing aims to design and develop natural human-user interfaces that respond to the emotional needs of the user, bridging the gap between humans and technology. With the continuing technological advancements affective computing technologies are now available at the consumer level and are revolutionizing the ways in which we interact with computers. From simple entertainment applications to assistive technologies, the field of affective computing holds great promise. The aim of this chapter is to provide the reader with a greater understanding of affective computing while highlighting current issues, example use cases, limitations, and areas of future research.

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Acknowledgements

The financial support of the N atural Sciences and Engineering Research Council of Canada (NSERC) and the Social Sciences and Humanities Research Council of Canada (SSHRC), is gratefully acknowledged.

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Software and Informatics Research Centre, Ontario Tech University, Oshawa, ON, Canada

Geoffrey Gaudi, Bill Kapralos & Alvaro Quevedo

maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada

Bill Kapralos

School of Information Technology, Carleton University, Ottawa, ON, Canada

K. C. Collins

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Correspondence to Bill Kapralos .

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Department of Informatics, University of Piraeus, Piraeus, Greece

Maria Virvou

George A. Tsihrintzis

Director of Applied Intelligent Systems Laboratory, Department of Nuclear Engineering, Purdue University, West Lafayette, IN, USA

Lefteri H. Tsoukalas

KES International, Shoreham-by-Sea, UK

Lakhmi C. Jain

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Gaudi, G., Kapralos, B., Collins, K., Quevedo, A. (2022). Affective Computing: An Introduction to the Detection, Measurement, and Current Applications. In: Virvou, M., Tsihrintzis, G.A., Tsoukalas, L.H., Jain, L.C. (eds) Advances in Artificial Intelligence-based Technologies. Learning and Analytics in Intelligent Systems, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-80571-5_3

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DOI : https://doi.org/10.1007/978-3-030-80571-5_3

Published : 03 October 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-80570-8

Online ISBN : 978-3-030-80571-5

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