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This 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.

Technology: general issues --- subject-dependent emotion recognition --- subject-independent emotion recognition --- electrodermal activity (EDA) --- deep learning --- convolutional neural networks --- automatic facial emotion recognition --- intensity of emotion recognition --- behavioral biometrical systems --- machine learning --- artificial intelligence --- driving stress --- electrodermal activity --- road traffic --- road types --- Viola-Jones --- facial emotion recognition --- facial expression recognition --- facial detection --- facial landmarks --- infrared thermal imaging --- homography matrix --- socially assistive robot --- EEG --- arousal detection --- valence detection --- data transformation --- normalization --- mental stress detection --- electrocardiogram --- respiration --- in-ear EEG --- emotion classification --- emotion monitoring --- elderly caring --- outpatient caring --- stress detection --- deep neural network --- convolutional neural network --- wearable sensors --- psychophysiology --- sensor data analysis --- time series analysis --- signal analysis --- similarity measures --- correlation statistics --- quantitative analysis --- benchmarking --- boredom --- emotion --- GSR --- classification --- sensor --- face landmark detection --- fully convolutional DenseNets --- skip-connections --- dilated convolutions --- emotion recognition --- physiological sensing --- multimodal sensing --- flight simulation --- activity recognition --- physiological signals --- thoracic electrical bioimpedance --- smart band --- stress recognition --- physiological signal processing --- long short-term memory recurrent neural networks --- information fusion --- pain recognition --- long-term stress --- electroencephalography --- perceived stress scale --- expert evaluation --- affective corpus --- multimodal sensors --- overload --- underload --- interest --- frustration --- cognitive load --- stress research --- affective computing --- human-computer interaction --- deep convolutional neural network --- transfer learning --- auxiliary loss --- weighted loss --- class center --- stress sensing --- smart insoles --- smart shoes --- unobtrusive sensing --- stress --- center of pressure --- regression --- signal processing --- arousal --- aging adults --- musical genres --- emotion elicitation --- dataset --- emotion representation --- feature selection --- feature extraction --- computer science --- virtual reality --- head-mounted display --- n/a