LEADER 06888nam 2201729z- 450 001 9910557346003321 005 20231214133638.0 035 $a(CKB)5400000000042430 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76513 035 $a(EXLCZ)995400000000042430 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmotion and Stress Recognition Related Sensors and Machine Learning Technologies 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (550 p.) 311 $a3-0365-1138-5 311 $a3-0365-1139-3 330 $aThis book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. 606 $aTechnology: general issues$2bicssc 610 $asubject-dependent emotion recognition 610 $asubject-independent emotion recognition 610 $aelectrodermal activity (EDA) 610 $adeep learning 610 $aconvolutional neural networks 610 $aautomatic facial emotion recognition 610 $aintensity of emotion recognition 610 $abehavioral biometrical systems 610 $amachine learning 610 $aartificial intelligence 610 $adriving stress 610 $aelectrodermal activity 610 $aroad traffic 610 $aroad types 610 $aViola-Jones 610 $afacial emotion recognition 610 $afacial expression recognition 610 $afacial detection 610 $afacial landmarks 610 $ainfrared thermal imaging 610 $ahomography matrix 610 $asocially assistive robot 610 $aEEG 610 $aarousal detection 610 $avalence detection 610 $adata transformation 610 $anormalization 610 $amental stress detection 610 $aelectrocardiogram 610 $arespiration 610 $ain-ear EEG 610 $aemotion classification 610 $aemotion monitoring 610 $aelderly caring 610 $aoutpatient caring 610 $astress detection 610 $adeep neural network 610 $aconvolutional neural network 610 $awearable sensors 610 $apsychophysiology 610 $asensor data analysis 610 $atime series analysis 610 $asignal analysis 610 $asimilarity measures 610 $acorrelation statistics 610 $aquantitative analysis 610 $abenchmarking 610 $aboredom 610 $aemotion 610 $aGSR 610 $aclassification 610 $asensor 610 $aface landmark detection 610 $afully convolutional DenseNets 610 $askip-connections 610 $adilated convolutions 610 $aemotion recognition 610 $aphysiological sensing 610 $amultimodal sensing 610 $aflight simulation 610 $aactivity recognition 610 $aphysiological signals 610 $athoracic electrical bioimpedance 610 $asmart band 610 $astress recognition 610 $aphysiological signal processing 610 $along short-term memory recurrent neural networks 610 $ainformation fusion 610 $apain recognition 610 $along-term stress 610 $aelectroencephalography 610 $aperceived stress scale 610 $aexpert evaluation 610 $aaffective corpus 610 $amultimodal sensors 610 $aoverload 610 $aunderload 610 $ainterest 610 $afrustration 610 $acognitive load 610 $astress research 610 $aaffective computing 610 $ahuman-computer interaction 610 $adeep convolutional neural network 610 $atransfer learning 610 $aauxiliary loss 610 $aweighted loss 610 $aclass center 610 $astress sensing 610 $asmart insoles 610 $asmart shoes 610 $aunobtrusive sensing 610 $astress 610 $acenter of pressure 610 $aregression 610 $asignal processing 610 $aarousal 610 $aaging adults 610 $amusical genres 610 $aemotion elicitation 610 $adataset 610 $aemotion representation 610 $afeature selection 610 $afeature extraction 610 $acomputer science 610 $avirtual reality 610 $ahead-mounted display 615 7$aTechnology: general issues 700 $aKyamakya$b Kyandoghere$4edt$01294150 702 $aAl-Machot$b Fadi$4edt 702 $aMosa$b Ahmad Haj$4edt 702 $aBouchachia$b Hamid$4edt 702 $aChedjou$b Jean Chamberlain$4edt 702 $aBagula$b Antoine$4edt 702 $aKyamakya$b Kyandoghere$4oth 702 $aAl-Machot$b Fadi$4oth 702 $aMosa$b Ahmad Haj$4oth 702 $aBouchachia$b Hamid$4oth 702 $aChedjou$b Jean Chamberlain$4oth 702 $aBagula$b Antoine$4oth 906 $aBOOK 912 $a9910557346003321 996 $aEmotion and Stress Recognition Related Sensors and Machine Learning Technologies$93036787 997 $aUNINA