Special Session on Computational Intelligence for Physiological and Affective Computing (CIPAC)


Affective Computing (AC) is “computing that relates to, arises from, or deliberately influences emotions,” as initially coined by Professor R. Picard (Media Lab, MIT). It has been gaining popularity rapidly in the last decade because it has great potential in the next generation of human-computer interfaces. One goal of affective computing is to design a computer system that responds in a rational and strategic fashion to real-time changes in user affect (e.g., happiness, sadness, etc.), cognition (e.g., frustration, boredom, etc.) and motivation, as represented by speech, facial expressions, gestures, physiological signals, neurocognitive performance, etc. Physiological Computing (PC) relates to computation that incorporates physiological signals in order to produce useful outputs (e.g., in computer-human interaction). It mainly differs from AC in the sense that its foremost focus is not the modeling of affect but rather the utilization of physiological information generally.


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 emotional states and physiological signals. 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 artifacts within.


The integration and use of AC and PC raise many new challenges for signal processing, machine learning and computational intelligence. Fuzzy Logic Systems in particular provide a highly promising avenue for addressing some of the fundamental research challenges in AC/PC where most data sources such as: body signals (e.g., heart rate, brain waves, skin conductance and respiration) facial features, speech and human kinematics are very noisy/uncertain and subject-dependent. Clearly however, other key areas of Computational Intelligence (CI) research, such as evolutionary learning algorithms and neural network based classifiers provide essential tools to address the significant challenge of AC/PC.


The Computational Intelligence for Physiological and Affective Computing special session is also organised through the IEEE Computational Intelligence Society's Emerging Technologies Task Force on Affective Computing. The session aims to bring together researchers from the three areas of CI to discuss how CI techniques can be used individually or in combination to help solve challenging AC/PC problems, and conversely, how physiological and affect (emotion) and its modeling can inspire new approaches in CI and its applications. Topics of interest for this special session 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 emotion 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.


As a cross-disciplinary and CI applications oriented special session, accepted and presented papers will be published under one of the three conference proceedings (Fuzzy-IEEE, IJCNN or IEEE CEC) that are most appropriate for the presented research. For paper submissions please visit WCCI IEEE 2016 submissions



Important Dates


Paper submission deadline: 31st January 2016

Author notification: 15th March 2016

Deadline for final manuscript: 15th April 2016

Early registration deadline: 15th April 2016

Conference dates: 25th July – 29th July 2016



Program Committee


Egon L. van den Broek, University of Twente, The Netherlands

Hani Hagras, University of Essex, UK

Marie-Jeanne Lesot, LIP6-UPMC, France

Chin-Teng Lin, National Chiao Tung University, Taiwan

Jiann-Shing Shieh, Yuan Ze University, Taiwan

Shrikanth Narayanan, University of Southern California, USA

Ana Paiva, Technical University of Lisbon, Portugal

Rahat Iqbal, Coventry University, UK

Dr. Vernon Lawhern, US Army Research Lab

Mei Si, Rensselaer Polytechnic Institute, USA

Carlo Strapparava, Fondazione Bruno Kessler, Italy

Shangfei Wang, University of Science and Technology of China, China

Dongrui Wu, GE Global Research, USA

Georgios Yannakakis, IT University of Copenhagen, Denmark

Slawomir Zadrozny, Polish academy of science, Poland

Michel Valstar, University of Nottingham, UK

Christopher Peters, KTH Royal Institute of Technology, Sweden

Palaniappan Ramaswamy, University of Kent, UK

Ahmed Kattan, Um Al-Qura University, Saudi Arabia

Brent Lance, US Army Research Lab

Victor Zamudio, Instituto Tecnologico de Leon, Mexico

Vasile Palade, Coventry University, UK

Li-Wei Ko, National Chiao Tung University, Taiwan

Nicolas Sabouret, University Paris-Sud, France

Maria Rifqi, University Panthéon-Assas, France





Dr Faiyaz Doctor

School of Computing, Electronics and Maths

Faculty of Engineering, Environment & Computing

Coventry University

Email: faiyaz.doctor AT coventry.ac.uk



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 40 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 Fuzzy Systems for Physiological and Affective Computing (FSPAC) at the 2015 IEEE International Conference on Fuzzy Systems and 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 has served as co-organiser of the International Workshop on Applications of Affective Computing in Intelligent Environments (ACIE 2013, 2014) 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

Email: christian.wagner AT nottingham.ac.uk



Christian Wagner is an Associate Professor in Computer Science at the University of Nottingham, UK. He received his PhD in Computer Science from the University of Essex in 2009 after which he was involved both in the management and scientific work of the EU FP7 project ATRACO, joining the University of Nottingham in 2011. His main research interests are centred on uncertainty handling, approximate reasoning (reasoning in the face of uncertainty, lack of knowledge and vagueness), decision support and data-driven decision making using computational intelligence techniques. Recent applications of his research have focused in particular on decision support in environmental and infrastructure planning & management contexts as well as cyber-security. He has published more than 60 peer-reviewed articles in international journals and conferences, two of which recently won best paper awards (Outstanding IEEE Transactions on Fuzzy Systems paper 2013 (for a paper in 2010) and a best paper award for a Fuzz-IEEE 2012 conference paper), and several book chapters. Dr Wagner is currently active PI and Co-I on a number of research projects, with overall funding as PI of £1 million and funding as Co-I of 2 million. He is an Associate Editor of the IEEE Transactions on Fuzzy Systems journal (IF: 6.3) and is actively involved in the academic community through for example the organization of special sessions at premiere IEEE conference such as the World Congress on Computational Intelligence 2014 and the IEEE Conference on System, Man and Cybernetics 2015. He has developed and been involved in the creation of multiple open source software frameworks, making cutting edge research accessible both to peer researchers as well as to different (multidisciplinary - beyond computer science) research and practitioner communities, including R and Java based toolkits for type-2 fuzzy systems in use in more than ten countries.



Dr Dongrui Wu

DataNova, NY, USA

Email: drwu09 AT gmail.com



Dongrui Wu received a PhD in Electrical Engineering from the University of Southern California (USC) in 2009. He was a Research Associate in the USC Institute for Creative Technologies and Signal Analysis & Interpretation Laboratory (SAIL) 2009-2010, and a Lead Research Engineer in the Machine Learning Lab of GE Global Research 2010-2015. Now he is starting his own business. Dr. Wu's research interests include affective computing, brain-computer interaction, computational intelligence, intelligent control, machine learning, optimization, and text analysis. He has over 80 publications, including a book "Perceptual Computing." He received IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award in 2012, IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, and NAFIPS Early Career Award in 2014. He was a selected participant of the 1st Heidelberg Abel/Fields/Turing Laureate Forum in 2013, NAE 2015 German-American Frontiers of Engineering, and the 13th Annual National Academies Keck Futures Initiative (NAKFI) conference in 2015. Dr. Wu had worked on a broad range of projects from GE Capital, Healthcare, Transportation, Power and Water, and Oil & 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 received 10 Above and Beyond Awards for outstanding performance. Dr. Wu is an Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Human-Machine Systems, and PeerJ Computer Science. He was the lead Guest Editor of the IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence and Affective Computing, and former Chair of the IEEE CIS Affective Computing Task Force. He is a Senior Member of IEEE, and an Executive Committee member of the Association for the Advancement of Affective Computing (AAAC).



Dr Marie-Jeanne Lesot

Université Pierre et Marie Curie

Laboratoire d'Informatique de Paris 6, LIP6

Email: Marie-Jeanne.Lesot AT lip6.fr



Marie-Jeanne Lesot obtained her PhD in 2005 from the University Pierre et Marie Curie in Paris. Since 2006 she has been an associate professor in the department of Computer Science Lab of Paris 6 (LIP6) and a member of the Learning and Fuzzy Intelligent systems (LFI) group. Her research interests focus on fuzzy machine learning with an objective of data interpretation and semantics integration and, in particular, to model and manage subjective information; they include similarity measures, fuzzy clustering, linguistic summaries, affective computing and information scoring. She has published 43 international journal and conference papers and co-edited a book on scalable fuzzy algorithms for data management and analysis. She has also participated in various joint projects with companies, on topics such as fraud detection, dynamic monitoring of information propagation, emotional design and emotion mining among others.