Robust Multimodal Cognitive Load Measurement

Robust Multimodal Cognitive Load Measurement
Springer | HCI | July 16, 2016 | ISBN-10: 3319316982 | 200 pages | pdf | 9.39 mb

Authors: Chen, F., Zhou, J., Wang, Y., Yu, K., Arshad, S.Z., Khawaji, A., Conway, D.

Explores multimodal cognitive load measurement using physiological and behavioral modalities
Introduces various computational methods for automatic and real-time cognitive load measurement
Provides models for dynamic workload adjustment and real-time cognitive load measurement via data streaming

This book explores robust multimodal cognitive load measurement with physiological and behavioural modalities, which involve the eye, Galvanic Skin Response, speech, language, pen input, mouse movement and multimodality fusions. Factors including stress, trust, and environmental factors such as illumination are discussed regarding their implications for cognitive load measurement. Furthermore, dynamic workload adjustment and real-time cognitive load measurement with data streaming are presented in order to make cognitive load measurement accessible by more widespread applications and users. Finally, application examples are reviewed demonstrating the feasibility of multimodal cognitive load measurement in practical applications.

This is the first book of its kind to systematically introduce various computational methods for automatic and real-time cognitive load measurement and by doing so moves the practical application of cognitive load measurement from the domain of the computer scientist and psychologist to more general end-users, ready for widespread implementation.

Robust Multimodal Cognitive Load Measurement is intended for researchers and practitioners involved with cognitive load studies and communities within the computer, cognitive, and social sciences. The book will especially benefit researchers in areas like behaviour analysis, social analytics, human-computer interaction (HCI), intelligent information processing, and decision support systems.

Number of Illustrations and Tables

66 b/w illustrations, 65 illustrations in colour

Topics

User Interfaces and Human Computer Interaction

Biological Psychology

Biometrics

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Robust Intelligence and Trust in Autonomous Systems

Robust Intelligence and Trust in Autonomous Systems
Springer | Artificial Intelligence | May 10 2016 | ISBN-10: 1489976663 | 270 pages | pdf | 6.41 mb

Editors: Mittu, R., Sofge, D., Wagner, A., Lawless, W.F. (Eds.)

Explores the effective integration of human-autonomous systems
Examines the implications of gaps that must be tackled to enable a successful integration of autonomous and human systems
Written by leading international experts on the topic

This volume explores the intersection of robust intelligence (RI) and trust in autonomous systems across multiple contexts among autonomous hybrid systems, where hybrids are arbitrary combinations of humans, machines and robots. To better understand the relationships between artificial intelligence (AI) and RI in a way that promotes trust between autonomous systems and human users, this book explores the underlying theory, mathematics, computational models, and field applications. It uniquely unifies the fields of RI and trust and frames it in a broader context, namely the effective integration of human-autonomous systems.

A description of the current state of the art in RI and trust introduces the research work in this area. With this foundation, the chapters further elaborate on key research areas and gaps that are at the heart of effective human-systems integration, including workload management, human computer interfaces, team integration and performance, advanced analytics, behavior modeling, training, and, lastly, test and evaluation.

Written by international leading researchers from across the field of autonomous systems research, Robust Intelligence and Trust in Autonomous Systems dedicates itself to thoroughly examining the challenges and trends of systems that exhibit RI, the fundamental implications of RI in developing trusted relationships with present and future autonomous systems, and the effective human systems integration that must result for trust to be sustained.

Contributing authors: David W. Aha, Jenny Burke, Joseph Coyne, M.L. Cummings, Munjal Desai, Michael Drinkwater, Jill L. Drury, Michael W. Floyd, Fei Gao, Vladimir Gontar, Ayanna M. Howard, Mo Jamshidi, W.F. Lawless, Kapil Madathil, Ranjeev Mittu, Arezou Moussavi, Gari Palmer, Paul Robinette, Behzad Sadrfaridpour, Hamed Saeidi, Kristin E. Schaefer, Anne Selwyn, Ciara Sibley, Donald A. Sofge, Erin Solovey, Aaron Steinfeld, Barney Tannahill, Gavin Taylor, Alan R. Wagner, Yue Wang, Holly A. Yanco, Dan Zwillinger.

Number of Illustrations and Tables

25 b/w illustrations, 64 illustrations in colour

Topics

Artificial Intelligence (incl. Robotics)

Robotics and Automation

Computational Intelligence

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