Autonomic nervous system dynamics for mood and emotional-state recognition [electronic resource] : significant advances in data acquisition, signal processing and classification / Gaetano Valenza, Enzo Pasquale Scilingo.
2014
QP368
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Title
Autonomic nervous system dynamics for mood and emotional-state recognition [electronic resource] : significant advances in data acquisition, signal processing and classification / Gaetano Valenza, Enzo Pasquale Scilingo.
Author
ISBN
9783319026398 electronic book
3319026399 electronic book
9783319026381
3319026399 electronic book
9783319026381
Published
Cham : Springer, 2014.
Language
English
Description
1 online resource (xix, 162 pages) : illustrations (some color)
Item Number
10.1007/978-3-319-02639-8 doi
Call Number
QP368
Dewey Decimal Classification
612.8/9
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed November 4, 2013).
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Series
Series in bioengineering, 2196-8861
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Table of Contents
Emotions and Mood States: Modeling, Elicitation, and Classification through Autonomic Patterns
Gathering Data from the Autonomic Nervous System: Experimental Procedures and Wearable Monitoring Systems
Methodology of Advanced Signal Processing and Modeling
Experimental Evidences on Healthy Subjects and Bipolar Patients
Discussion on mood and emotional-state recognition using the Autonomic Nervous System Dynamics
Summary of the Book and Direction for Future Research.
Gathering Data from the Autonomic Nervous System: Experimental Procedures and Wearable Monitoring Systems
Methodology of Advanced Signal Processing and Modeling
Experimental Evidences on Healthy Subjects and Bipolar Patients
Discussion on mood and emotional-state recognition using the Autonomic Nervous System Dynamics
Summary of the Book and Direction for Future Research.