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000695976 020__ $$a9783319026398 $$qelectronic book
000695976 020__ $$a3319026399 $$qelectronic book
000695976 020__ $$z9783319026381
000695976 0247_ $$a10.1007/978-3-319-02639-8$$2doi
000695976 035__ $$aSP(OCoLC)ocn865008489
000695976 035__ $$aSP(OCoLC)865008489
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000695976 049__ $$aISEA
000695976 050_4 $$aQP368
000695976 08204 $$a612.8/9$$223
000695976 1001_ $$aValenza, Gaetano,$$eauthor.
000695976 24510 $$aAutonomic nervous system dynamics for mood and emotional-state recognition$$h[electronic resource] :$$bsignificant advances in data acquisition, signal processing and classification /$$cGaetano Valenza, Enzo Pasquale Scilingo.
000695976 264_1 $$aCham :$$bSpringer,$$c2014.
000695976 300__ $$a1 online resource (xix, 162 pages) :$$billustrations (some color)
000695976 336__ $$atext$$btxt$$2rdacontent
000695976 337__ $$acomputer$$bc$$2rdamedia
000695976 338__ $$aonline resource$$bcr$$2rdacarrier
000695976 4901_ $$aSeries in BioEngineering,$$x2196-8861
000695976 504__ $$aIncludes bibliographical references and index.
000695976 5050_ $$aEmotions and Mood States: Modeling, Elicitation, and Classification through Autonomic Patterns -- Gathering Data from the Autonomic Nervous System: Experimental Procedures and Wearable Monitoring Systems -- Methodology of Advanced Signal Processing and Modeling -- Experimental Evidences on Healthy Subjects and Bipolar Patients -- Discussion on mood and emotional-state recognition using the Autonomic Nervous System Dynamics -- Summary of the Book and Direction for Future Research.
000695976 506__ $$aAccess limited to authorized users.
000695976 520__ $$aThis monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition proposes a novel probabilistic approach based on the point-process theory in order to model and characterize the instantaneous ANS nonlinear dynamics providing a foundation from which machine understanding of emotional response can be enhanced. Using mathematics and signal processing, this work also contributes to pragmatic issues such as emotional and mood-state modeling, elicitation, and non-invasive ANS monitoring. Throughout the text a critical review on the current state-of-the-art is reported, leading to the description of dedicated experimental protocols, novel and reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment. Biomedical engineers will find this book of interest, especially those concerned with nonlinear analysis, as will researchers and industrial technicians developing wearable systems and sensors for ANS monitoring.
000695976 588__ $$aDescription based on online resource; title from PDF title page (SpringerLink, viewed November 4, 2013).
000695976 650_0 $$aAutonomic nervous system.
000695976 650_0 $$aAffective disorders.
000695976 650_0 $$aEmotions.
000695976 7001_ $$aScilingo, Enzo Pasquale,$$eauthor.
000695976 830_0 $$aSeries in bioengineering,$$x2196-8861
000695976 85280 $$bebk$$hSpringerLink
000695976 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-02639-8$$zOnline Access
000695976 909CO $$ooai:library.usi.edu:695976$$pGLOBAL_SET
000695976 980__ $$aEBOOK
000695976 980__ $$aBIB
000695976 982__ $$aEbook
000695976 983__ $$aOnline
000695976 994__ $$a92$$bISE