rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Robust Multimodal Cognitive Load Measurement
  name:
    name part: Chen, Fang.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Zhou, Jianlong.
    role:
      role term: author.
  name:
    name part: Wang, Yang.
    role:
      role term: author.
  name:
    name part: Yu, Kun.
    role:
      role term: author.
  name:
    name part: Arshad, Syed Z.
    role:
      role term: author.
  name:
    name part: Khawaji, Ahmad.
    role:
      role term: author.
  name:
    name part: Conway, Dan.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 254 p. 131 illus., 65 illus. in color. online resource.
  abstract: 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.
  table of contents: Preface -- Part I: Preliminaries -- Introduction -- The State-of-The-Art -- Theoretical Aspects of Multimodal Cognitive Load Measures -- Part II: Physiological Measurement -- Eye Based Measures. Galvanic Skin Response Based Measures -- Part III: Behavioural Measurement -- Linguistic Feature Based Measures -- Speech Signal Based Measures -- Pen Input Based Measures -- Mouse Based Measures -- Part IV: Multimodal Measures and Affecting Factors -- Multimodal Measures and Data Fusion -- Emotion and Cognitive Load -- Stress and Cognitive Load -- Trust and Cognitive Load -- Part V: Making Cognitive Load Measurement Accessible -- Dynamic Cognitive Load Adjustments in A Feedback Loop -- Real-Time Cognitive Load Measurement: Data Streaming Approach -- Applications of Cognitive Load Measurement -- Part VI: Conclusions -- Cognitive Load Measurement in Perspectives.
  note: by Fang Chen, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z. Arshad, Ahmad Khawaji, Dan Conway.
  subject:
    topic: Computer science
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Biometrics (Biology)
  subject:
    topic: Biological psychology
  subject:
    topic: Computer Science
  subject:
    topic: User Interfaces and Human Computer Interaction
  subject:
    topic: Biological Psychology
  subject:
    topic: Biometrics
  classification: QA76.9.U83
  classification: QA76.9.H85
  classification: 005.437
  classification: 4.019
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Human–Computer Interaction Series
  identifier: 9783319317007
  identifier: http://dx.doi.org/10.1007/978-3-319-31700-7
  record info:
    record creation date: 160614
    record change date: 20160614162249.0
    record identifier: 978-3-319-31700-7