
Contents
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Introduction Introduction
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Survey of Tools for Affective Computing Survey of Tools for Affective Computing
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Software Tools in Affective Computing Software Tools in Affective Computing
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Programming Languages and Software Platforms Programming Languages and Software Platforms
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Labeling and Data Annotation Labeling and Data Annotation
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Signal Processing and Analysis Signal Processing and Analysis
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Data Mining and Affect Classification Data Mining and Affect Classification
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Affect Expression Affect Expression
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Data Collection Data Collection
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Summary Summary
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Acknowledgments Acknowledgments
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References References
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25 Research and Development Tools in Affective Computing
Get accessM. Sazzad Hussain, PhD (2013) is a Postdoctoral Research Fellow with the Software Engineering Group at The University of Sydney. His research interests are affective computing, learning technologies, human-computer interaction, and systems to support mental health problems. He has worked in a variety of research projects and is author of over 30 publications. He has a PhD in multimodal affect and cognitive load detection from The University of Sydney and has also worked at National ICT Australia (NICTA) as part of his doctoral research. He had received the prestigious PhD scholarship Endeavour Postgraduate Award by Australia Awards in 2009. Sazzad was also awarded best student in 2008 for his academic achievements in MSc.
Sidney D’Mello is an Assistant Professor in the departments of Computer Science and Psychology at the University of Notre Dame. His primary research interests are in the affective, cognitive, learning, and computing sciences. More specific interests include affective computing, artificial intelligence in education, human-computer interaction, natural language understanding, and computational models of human cognition. He has co-edited six books and has published over 150 journal papers, book chapters, and conference proceedings in these areas. D’Mello’s work has received seven outstanding paper awards at international conferences, has been featured in several media outlets including the Wall Street Journal, and has been supported by the National Science Foundation, Institute for Education Sciences, Gates, Raikes, and Templeton Foundations. D’Mello is an associate editor for IEEE Transactions on Affective Computing and IEEE Transactions on Learning Technologies, a senior reviewer for the Journal of Educational Psychology, and serves on the executive board of the International Artificial Intelligence in Education Society. D’Mello received his PhD. in Computer Science from the University of Memphis in 2009. He also holds a M.S. in Mathematical Sciences and a B.S. in Electrical Engineering.
Rafael A. Calvo, PhD (2000) is Associate Professor at the University of Sydney’s and Director of the Software Engineering Group that focuses on the design of systems that support wellbeing in areas of mental health, medicine and education. He has a PhD in Artificial Intelligence applied to automatic document classification and has also worked at Carnegie Mellon University, Universidad Nacional de Rosario, and as a consultant for projects worldwide. Rafael is author of over 150 publications in the areas of affective computing, learning systems and web engineering, recipient of five teaching awards, and a Senior Member of IEEE. Rafael is Associate Editor of IEEE Transactions on Affective Computing and of IEEE Transactions on Learning Technologies.
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Published:01 July 2014
Cite
Abstract
This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. There are a wide variety of software tools that automate various aspects of affective computing research. Researchers can increase the productivity and reproducibility of their work by reusing appropriate tools. This chapter aims to identify and describe the common tools in affective computing research. Eight hundred affective computing researchers were surveyed in order to obtain information related to the software tools used in their research. The survey collected information on programming languages and tools for data annotation, signal analysis, pattern recognition for affect classification, affect expression, and data collection. The responses of the 66 individuals who completed the survey highlight the most common tools used in affective computing research.
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