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Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition

Significant Advances in Data Acquisition, Signal Processing and Classification

  • Gaetano Valenza
  • Enzo Pasquale Scilingo

Part of the Series in BioEngineering book series (SERBIOENG)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Introductory Remarks and State of the Art

    1. Front Matter
      Pages 1-1
    2. Gaetano Valenza, Enzo Pasquale Scilingo
      Pages 9-21
  3. Methodology

    1. Front Matter
      Pages 23-23
    2. Gaetano Valenza, Enzo Pasquale Scilingo
      Pages 25-43
    3. Gaetano Valenza, Enzo Pasquale Scilingo
      Pages 45-82
  4. Results

    1. Front Matter
      Pages 83-83
    2. Gaetano Valenza, Enzo Pasquale Scilingo
      Pages 85-123
  5. Conclusions and Future Works

    1. Front Matter
      Pages 125-125
    2. Gaetano Valenza, Enzo Pasquale Scilingo
      Pages 139-143
  6. Back Matter
    Pages 145-162

About this book

Introduction

This monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems.
Applicative studies of biosignals such as:
  • electrocardiograms;
  • electrodermal responses;
  • respiration activity;
  • gaze points; and
  • pupil-size variation

are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS.
Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition proposes a novel probabilistic approach based on the point-process theory in order to model and characterize the instantaneous ANS nonlinear dynamics providing a foundation from which machine “understanding” of emotional response can be enhanced.
Using mathematics and signal processing, this work also contributes to pragmatic issues such as emotional and mood-state modeling, elicitation, and non-invasive ANS monitoring. Throughout the text a critical review on the current state-of-the-art is reported, leading to the description of dedicated experimental protocols, novel and reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment.
Biomedical engineers will find this book of interest, especially those concerned with nonlinear analysis, as will researchers and industrial technicians developing wearable systems and sensors for ANS monitoring.

Keywords

Advanced Statistical and Nonlinear Signal Processing Affective Computing Autonomic Nervous System Dynamics Emotion Recognition Mood Recognition Physiological Modeling Wearable Monitoring Systems

Authors and affiliations

  • Gaetano Valenza
    • 1
  • Enzo Pasquale Scilingo
    • 2
  1. 1.Bioengineering and Robotics Research Center “E. Piaggio”University of PisaPisaItaly
  2. 2.Bioengineering and Robotics Research Center “E. Piaggio”University of PisaPisaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-02639-8
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-02638-1
  • Online ISBN 978-3-319-02639-8
  • Series Print ISSN 2196-8861
  • Series Online ISSN 2196-887X
  • Buy this book on publisher's site