Special Session on Fuzzy Systems for Physiological and Affective Computing (FSPAC)

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, flavours 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 powerful 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 is also organised through the IEEE Computational Intelligence Society's Emerging Technologies Task Force on Affective Computing.The 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 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.


For paper submissions please visit FUZZ IEEE 2015 submissions

Important Dates

Paper submission deadline: 8th February 2015

Author notification: 23rd March 2015

Deadline for final manuscript: 21st April 2015

Early registration deadline for final manuscript: 23rd April 2015

Conference dates: 2nd August – 5th August 2015



Program Committee

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

Hani Hagras, University of Essex, UK

Marie-Jeanne Lesot, LIP6-UPMC, France

Peter Lewis, Aston University, UK

Chin-Teng Lin, National Chiao Tung University, Taiwan

Jiann-Shing Shieh, Yuan Ze University, Taiwan

Shrikanth Narayanan, University of Southern California, USA

Anton Nijholt, University of Twente, The Netherlands

Ana Paiva, Technical University of Lisbon, Portugal

Rahat Iqbal, Coventry University, UK

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

Guillaume Chanel, Swiss Center for Affective Science, Switzerland

Victor Zamudio, Instituto Tecnologico de Leon, Mexico

Ginevra Catellano, University of Birmingham, UK

Vasile Palade, Coventry University, UK





Faiyaz Doctor

Department of Computing

Faculty of Engineering and Computing

Coventry University, UK

faiyaz.doctor AT coventry.ac.uk





Christian Wagner

Horizon Digital Economy Institute & Intelligent Modeling and Analysis Group

School of Computer Science

University of Nottingham, UK

christian.wagner AT nottingham.ac.uk


Dongrui Wu

Machine Learning Lab

GE Global Research, Niskayuna, NY, USA

drwu0 AT gmail.com