Wednesday, 3 December 2014

Call for Papers FUZZ-IEEE Special Session "Fuzzy Systems for Physiological and Affective Computing"

Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions” (R. Picard, MIT Media Lab). AC seeks to facilitate research through the recognition and modelling of human affective states (e.g., happiness, sadness, etc.), cognition (e.g., frustration, boredom, etc.) and motivation, as represented by speech, facial expressions, physiological signals, and neurocognitive performance. Physiological Computing (PC) relates to more generic computational systems that incorporates and utilizes physiological information (e.g., as in computer-human interaction). Practical applications of AC and PC based systems seek to achieve a positive impact on our everyday lives by monitoring, recognising and acting on our physiological signals, speech, facial expressions and gestures. Integrating these sensing modalities into intelligent and pervasive computing systems will reveal a far richer picture of how our fleeting emotional responses, changing moods, feelings and sensations, such as pain, touch, tastes and smells, are a reaction to or influence how we implicitly or explicitly interact with the environment and increasingly the connected computing artefacts within.

The integration and use of AC and PC raise new challenges for signal processing, machine learning and Computational Intelligence (CI). Fuzzy systems provide a useful methodology for addressing research challenges in AC/PC, where data sources such as: body signals (e.g. heart rate, brain waves, skin conductance and respiration), facial features, speech and human kinematics are inherently noisy and uncertain. Fuzzy systems are well suited to model and represent vague and ambiguous linguistic notions of perceptions, impulses, feelings, desires and human cognitive states which can be both subject and context dependent. As we develop better ways of integrating affective and physiological data pervasively and in context of diverse data sources, it will create highly complex, dynamic and uncertain information rich scenarios. Here the use of hybrid fuzzy approaches combining other CI approaches such as evolutionary algorithms and neural computing techniques can be used to create novel self-learning affective computing systems that are able to more naturally interact and empathize with people, understand their physical and emotive states and automatically respond in beneficial and useful ways.

The Fuzzy Systems for Physiological and Affective Computing special session aims to bring together researchers to discuss how fuzzy logic approaches can be used to help solve challenging AC/PC problems, and develop ways of modelling and using physiological and affect (emotion) information to inspire new approaches and applications. We encourage high quality publications related to both academic and commercial research where topics of interest can include but are not limited to:
  • Models of emotion and physiological information
  • Classifiers for physiological information 
  • Applications based on/around physiological information 
  • Fuzzy set and system based architectures for processing emotions and other affective states 
  • Automatic emotion recognition & synthesis from physiological signals, facial expressions, body language, speech, or neurocognitive performance 
  • Multimodal sensor fusion for emotional recognition 
  • Emotion mining from texts, images, or videos 
  • Affective interaction with virtual agents and robots based on fuzzy systems 
  • Physiological and emotion driven control. 
  • Applications of affective computing in interactive learning, affective gaming, personalized robotics, virtual reality, social networking, smart environments, healthcare and behavioral informatics, etc.


Affective computing, physiological computing, classification of physiological signals, affective interaction, emotion modeling, emotion recognition/synthesis, emotion mining, fuzzy systems, computational intelligence

Short biography of the organizer(s) and contact information:

The organizers have a track record at organizing and chairing special sessions at a number of previous IEEE conferences, including WCCI 2014, Fuzz-IEEE 2013, WCCI 2012, FUZZ-IEEE 2011, WCCI 2010 and FUZZ-IEEE 2009.

Dr Faiyaz Doctor

Senior Lecturer in Computing
Faculty of Engineering and Computing
Coventry University
Priory Street, Coventry
Dr. Faiyaz Doctor is a Senior Lecturer in the Faculty of Engineering and Computing at Coventry University. He has previously worked jointly in industry and academia to develop novel artificial intelligence solutions for addressing real world problems related to smart environments, energy optimization, predictive analytics and decision support. His work has resulted in high profile innovation awards (Best KTP Regional Finalist 2011, Load Stafford Award for Innovation) and an international patent on improved approaches for data analysis and decision-making using hybrid neuro-fuzzy and type-2 fuzzy systems: WO/2009/141631. He has previously been a co-investigator on a Technology Strategy Board funded project on developing driver prediction models using artificial intelligence approaches in collaboration with Jaguar Land Rover Ltd. His research interests are in the area of computational intelligence with an emphasis on fuzzy logic, type-2 fuzzy logic and hybrid systems where his research has been applied to ambient intelligence, pervasive and affective computing, industrial automation and biomedical systems. Dr. Doctor has published over 30 papers in peer reviewed international journals, conferences and workshops. He currently chairs the IEEE Computational Intelligence Society’s Emergent Technologies ‘Affective Computing’ Task Force and has been co-organizer of the special session on Computational Intelligence for Physiological and Affective Computing (CIPAC) and co-chair at the special session on Brain and Physiological Computation for Affective Computing at the 2014 IEEE World Congress on Computational Intelligence. He has been a guest editor for the Journal of Ambient Intelligence and Smart Environments (JAISE), Thematic Issue on Affect Aware Ubiquitous Computing and serves as co-organiser of the International Workshop on Applications of Affective Computing in Intelligent Environments (ACIE) in conjunction with the International Conference on Intelligent Environments. He is also and member of the IEEE and IEEE Computational Intelligence Society.

Dr Christian Wagner

Horizon Digital Economy Institute & Intelligent Modeling and Analysis Group
School of Computer Science
University of Nottingham, UK
Christian Wagner is Transitional Fellow in Computer Science at the Horizon Digital Economy Research Institute and the School of Computer Science at the University of Nottingham, UK. He received his MSc in Robotics and Embedded Systems and his PhD in Computer Science at the University of Essex in 2006 and 2009 respectively. His main research interests are type-2 fuzzy logic, uncertainty handling and computational intelligence in general. He is specifically interested in the theoretical and practical aspects of general type-2 fuzzy logic and the application of fuzzy logic for approximate reasoning and the modeling of humans and human concepts such as emotions and language. The application of his research has been mainly focused on applications in robotics, ambient intelligence and most recently on digital economy and affective computing applications. He has published more than 40 papers and articles in international journals and at international conferences as well as three book chapters. In 2012 shared the Fuzz-IEEE best paper award. He also won the 2012 IEEE Transactions on Fuzzy Systems Outstanding paper award for the best paper published in 2010. He is chair of the IEEE CIS Task Force on Affective Computing. He has been a special session co-organizer and chair at the International Conference on Fuzzy Systems from 2009-2013. He has been an invited panelist in the “Future of type-2 fuzzy logic” panel held in London in 2010 and the publication chair of the UK Workshop on Computational Intelligence 2010. He has been a programme committee member of several international conferences and a reviewer for several international conferences and journals such as the IEEE Transactions on Fuzzy Systems and International Journal of Robotics and Automation. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computational Intelligence Society. Over several years, he has held placements with several companies, including SES (Astra), Sun Microsystems and GE Fanuc Automation.

Dr Dongrui Wu

Machine Learning Lab
GE Global Research, Niskayuna, NY, USA
Dongrui Wu has been a Research Engineer in the Machine Learning Lab, GE Global Research, Niskayuna, NY, since 2010. He received a B.E in Automatic Control from the University of Science and Technology of China (USTC), Hefei, Anhui, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore (NUS), Singapore, in 2005, and a PhD in Electrical Engineering from the University of Southern California (USC), Los Angeles, CA, in 2009. He was a Research Associate in the USC Institute for Creative Technologies and Signal Analysis and Interpretation Laboratory.

Dongrui Wu's research interests include affective computing, brain-computer interaction, computational intelligence, intelligent control, machine learning, optimization, and speech and physiological signal processing. Dongrui Wu has over 70 publications, including a book “Perceptual Computing” (with J.M. Mendel, Wiley-IEEE, 2010). He received IEEE International Conference on Fuzzy Systems Best Student Paper Award in 2005, IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award in 2012, IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, and North American Fuzzy Information Processing Society (NAFIPS) Early Career Award in 2014. He was one of 200 selected young researchers worldwide to attend the 1st Heidelberg (Abel/Fields/Turing) Laureate Forum in 2013, and is one of 30 selected U.S. engineers to attend the National Academy of Engineering 2015 German-American Frontiers of Engineering Symposia.

Dongrui Wu has worked on a broad range of projects from GE Capital, Healthcare, Transportation, Power and Water, and Oil and Gas. Two of his projects won the prestigious CIO 100 Awards in 2012 (TrueSense for GE Water) and 2014 (Fleet Optimizer for GE Capital), respectively. Additionally, he has received nine Above and Beyond Awards for outstanding performance. He is now Principal Investigator of a U.S. Army Research Lab funded project on machine learning for EEG based brain-computer interface, and a major contributor in GE Capital's stress testing team.

Dongrui Wu is an Associate Editor of IEEE Transactions on Fuzzy Systems and IEEE Transactions on Human-Machine Systems, and an Editorial Board member of PeerJ Computer Science and Journal of Applied Computer Science. He was a Guest Editor of the IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence and Affective Computing. He is a Senior Member of IEEE, an Executive Committee member of the Association for the Advancement of Affective Computing (AAAC), and a member of IEEE Systems, Man and Cybernetics Society Brain-Machine Interface Systems Technical Committee, IEEE CIS Fuzzy Systems Technical Committee, Emergent Technologies Technical Committee, and Intelligent Systems Applications Technical Committee. He was Chair of the IEEE CIS Affective Computing Task Force in 2012, and has been a Vice Chair since 2013.

Important Dates

  • Paper submission February 8, 2015
  • Notification of acceptance for papers March 23, 2015
  • Camera-ready paper submission April 21, 2015
  • Early registration deadline April 23, 2015
  • Conference August 2-5, 2015

Submission of the papers

Please submit your papers for this special session to both the organizers and conference online submission system ( by indicating the title of the special session.

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