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Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition - Significant Advances in Data Acquisition, Signal Processing and Classification - Valenza & Scilingo

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  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. 						

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