000 04461nam a22005655i 4500
001 978-3-319-02639-8
003 DE-He213
005 20200421111853.0
007 cr nn 008mamaa
008 131029s2014 gw | s |||| 0|eng d
020 _a9783319026398
_9978-3-319-02639-8
024 7 _a10.1007/978-3-319-02639-8
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC009000
_2bisacsh
082 0 4 _a610.28
_223
100 1 _aValenza, Gaetano.
_eauthor.
245 1 0 _aAutonomic Nervous System Dynamics for Mood and Emotional-State Recognition
_h[electronic resource] :
_bSignificant Advances in Data Acquisition, Signal Processing and Classification /
_cby Gaetano Valenza, Enzo Pasquale Scilingo.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIX, 162 p. 49 illus., 36 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSeries in BioEngineering,
_x2196-8861
505 0 _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.
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.
650 0 _aEngineering.
650 0 _aNeurosciences.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 0 _aBiomedical engineering.
650 0 _aBiological psychology.
650 1 4 _aEngineering.
650 2 4 _aBiomedical Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aBiological Psychology.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aComputational Intelligence.
650 2 4 _aNeurosciences.
700 1 _aScilingo, Enzo Pasquale.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319026381
830 0 _aSeries in BioEngineering,
_x2196-8861
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-02639-8
912 _aZDB-2-ENG
942 _cEBK
999 _c56206
_d56206