The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces: user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces that often include biosignals. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. It includes recent deep learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. These chapters discuss real-time multimodal analysis of emotion and social signals from various modalities, and perception of affective expression by users. Further chapters discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this rapidly expanding field. In the final section of this volume, experts exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade.
Show moreThe Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces: user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces that often include biosignals. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. It includes recent deep learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. These chapters discuss real-time multimodal analysis of emotion and social signals from various modalities, and perception of affective expression by users. Further chapters discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this rapidly expanding field. In the final section of this volume, experts exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade.
Show moreSharon Oviatt (Incaa Designs) is internationally
known for her multidisciplinary work on multimodal and mobile
interfaces, human-centered interfaces, educational interfaces and
learning analytics. She has been recipient of the inaugural
ACM-ICMI Sustained Accomplishment Award, National Science
Foundation Special Creativity Award, and ACM-SIGCHI CHI Academy
award. She has published over 160 scientific articles in a wide
range of venues, and is an Associate Editor of the main journals
and edited book collections in the field of human-centered
interfaces. Her other books include The Design of Future
Educational Interfaces (2013, Routledge) and The Paradigm Shift to
Multimodality in Contemporary Computer Interfaces (2015, Morgan
Claypool).
Björn Schuller (University of Passau and Imperial
College London) is currently Chair of Complex and Intelligent
Systems at University of Passau and Reader in Machine Learning at
Imperial College. He is best known for his work on
multisensorial/multimodal intelligent signal processing for
affective, behavioral, and human-centered computing. In 2015 and
2016, he was honored by the World Economic Forum as one of 40/50
extraordinary scientists under age 40. His further awards include
the CHiME, MediaEval, and MIREX competitions. He has published over
500 peer-reviewed scientific contributions across a range of
disciplines and venues, and is Editor-in-Chief of IEEE Transactions
on Affective Computing. His books include Intelligent Audio
Analysis (2013, Springer) and Computational Paralinguistics (2013,
Wiley).
Philip Cohen (VoiceBox Technologies) is Senior
Vice President and Chief Scientist for Artificial Intelligence,
whose research interests include multimodal interaction,
human-computer dialogue, and multi-agent systems. He is a Fellow of
the American Association for Artificial Intelligence, past
President of the Association for Computational Linguistics, and
recipient (with Hector Levesque) of an Inaugural Influential Paper
Award by the International Foundation for Autonomous Agents and
Multi-Agent Systems. He was the Founder of Adapx, Professor and
Co-Director of the Center for Human-Computer Communication in
Computer Science at Oregon Health and Science University, and
Director of Natural Language in the Artificial Intelligence Center
at SRI International.
Daniel Sonntag (German Research Center for
Artificial Intelligence, DFKI) is a Principal Researcher and
Research Fellow. His research interests include multimodal and
mobile AI-based interfaces, common-sense modeling, and semantic
machine learning methods for cognitive computing and improved
usability. He has published over 120 scientific articles, and has
been recipient of the German High Tech Champion Award in 2011 and
the AAAI Recognition and IAAI Deployed Application Award in 2013.
He is an editorial board member of the German Journal on Artificial
Intelligence (KI). Currently, he leads both national and European
projects from the Federal Ministry of Education and Research, the
Federal Ministry for Economic Affairs and Energy, and Horizon
2020.
Gerasimos Potamianos (University of Thessaly) is
Associate Professor and Director of Graduate Studies in Electrical
and Computer Engineering. His research spans multisensory and
multimodal speech processing and scene analysis, with applications
to human-computer interaction and ambient intelligence. He has
authored over 120 articles, and has seven patents. He holds a
Diploma Degree from the National Technical University of Athens,
and a M.Sc. and Ph.D. from the Johns Hopkins University, all in
Electrical and Computer Engineering. In addition to his academic
experience, he has worked at AT&T Research Labs, IBM T.J.
Watson Research Center (US), and at the FORTH and NCSR 'Demokritos'
Research Centers in Greece.
Antonio Krüger (Saarland University and DFKI GmbH)
is professor of Computer Science and Director of the Media
Informatics Program at Saarland University, as well as Scientific
Director of the Innovative Retail Laboratory at the German Research
Center for Artificial Intelligence (DFKI). His research areas focus
on intelligent user interfaces, and mobile and ubiquitous
context-aware systems. He has been General Chair of the Ubiquitous
Computing Conference, and Program Chair of MobileHCI, IUI, and
Pervasive Computing. He also is the Steering Committee Chair of
Intelligent User Interfaces (IUI), and an Associate Editor of the
journals User Modeling and User-Adapted Interaction and ACM
Interactive, Mobile, Wearable and Ubiquitous Technologies.
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