Title: Affective Computing in HCI
Introduction to workshop
Affective Computing (AC) has become an emerging and important branch of artificial intelligence. AC’s overarching goal is to create systems that can interpret the emotional state of humans and adapt its behaviour in order to provide intuitive and appropriate emotionally informed responses. The field of AC directly applies to the advancement of HCI and can involve the study and development of systems, techniques and devices that can recognise, interpret, process and manage human emotional states.
This workshop provides a forum for exchange and discussion on affective computing in HCI, such as emotion detection, human and machine interaction, mood tagging, emotion modelling, semantic representation, affective support and tutoring, psychophysiology (GSR, EEG, HRV studies), mental health and wellbeing, positive computing.
This workshop specifically addresses AC in a HCI context and the proposed workshop topics include, but are not limited to:
- AC analysis techniques in HCI
- Emotion recognition and detection algorithms
- Computational modelling for affective and emotional monitoring
- Facial/Speech/Text/Sentiment mining in emotion recognition
- AC in transportation HCI
- Emotion monitoring/tracking technology
- AC in the home environment
- AC in learning systems
- Mobile and cloud based AC
- Taking AC into the Wild
- AC challenges for HCI
- AC use cases in HCI
- Psychophysiology (GSR, EEG, HRV studies)
- Mental health and wellbeing using positive computing
- Cognate areas
For Submission Guidelines: See – http://hci2018.bcs.org/index.php/call-for-papers/
Program chairs or co-chairs:
Alfie Keary, Department of Computer Science, Cork Institute of Technology, Bishopstown, Cork, Ireland
Prof. Huiru (Jane) Zheng, Professor, School of Computing, Ulster University, UK. Email: firstname.lastname@example.org
Dr. Raymond Bond, Lecturer, School of Computing, Ulster University, UK. Email: email@example.com
Prof. Paul Walsh, Department of Computer Science, Cork Institute of Technology, Bishopstown, Cork, Ireland
Machine Learning Mediated Interaction for Health and Wellbeing
This talk explores how artificial intelligence and machine learning can be used to assist in health care scenarios and will demonstrate computer vision and other sensing technologies can be used to assess wellbeing.
Machine learning technology expert, academic and founder of life science software company www.nsilico.com, the provider of biomedical informatics software that rapidly accelerates innovation. Project manager expert and principal investigator funded under major national and international research programs including FP7 and H2020. An editorial board member for major scientific publications, holding a PhD in science and a certified technology and project management professional with experience in consultancy for major international clients. Specialties: AI, machine learning, research, innovation, project management and entrepreneurship.
Empathic Communication in the Human Machine Interface
When humans successfully socially interact with each other they often talk about having made a real connection with one another or that "the other person really understood me". Here I will argue that this results from human communication being a means to display a "mind-reading" ability – in the scientific meaning of mind-reading rather than the theatrical. Human communication has goals that are less concerned with the provision of useful information, which is often the assumption in Human Computer Interaction research, and more concerned with displaying socio-political astuteness. This requires us to show understanding of other people, knowledge of their desires, goals, feelings, and expectations. Showing this understanding is the hallmark of the empathic communicator. Responding appropriately using this knowledge within a social context marks out the socially fit communicator from the socially awkward. This reframing of human communication has many implications for Human Machine and Human Computer Interaction. Effective interfaces appear to have an intuitive understanding of our expectations, and as we approach an era of personal digital assistants we require them to be able to respond appropriately within certain contexts. For these assistants to exist seamlessly within our environments they will require an empathic understanding of those they wish to assist and the ability to produce contextually appropriate social signals to display this understanding.
Gary McKeown is a senior lecturer and social psychologist in the School of Psychology, Queens University Belfast. He has a primary research and theoretical interest in human communication and social interaction. His research profile is interdisciplinary with both theoretical, experimental and methodological papers in psychology journals and also many within the domains of social signal processing and affective computing. Often this research involves understanding natural human communication to inform the development of embodied conversational agents. Building on a long history of emotion and affective computing research in the School of Psychology – including the HUMAINE network, he had important roles in the SEMAINE, ILHAIRE project and within the SSPNet network. He has produced a theoretical account of the evolution of human communication with a particular emphasis on mind-reading and perspective-taking known as the Analogical Peacock Hypothesis; this has led to research work in understanding laughter, humour and storytelling. His research interests have led to many collaborations with non-academic partners, particularly commercial and industrial partnerships for the transfer of knowledge and techniques to commercial settings. From a methodological perspective, the fields of social signal processing and affective computing address challenging data collection issues from a behavioural science stance–often using dynamic scenarios with multiple streams of synchronised information. This requires new ways of thinking about data and novel statistical approaches to develop analyses that can usefully address social science questions. Dr McKeown has been actively involved in the creation of both new data gathering techniques and the statistical approaches required to address them.