Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

• Autore: Kyamakya Kyandoghere
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (550 p.)
• Soggetto topico: Technology: general issues
• Soggetto non controllato: activity recognition; affective computing; affective corpus; aging adults; arousal; arousal detection; artificial intelligence; automatic facial emotion recognition; auxiliary loss; behavioral biometrical systems; benchmarking; boredom; center of pressure; class center; classification; cognitive load; computer science; convolutional neural network; convolutional neural networks; correlation statistics; data transformation; dataset; deep convolutional neural network; deep learning; deep neural network; dilated convolutions; driving stress; EEG; elderly caring; electrocardiogram; electrodermal activity; electrodermal activity (EDA); electroencephalography; emotion; emotion classification; emotion elicitation; emotion monitoring; emotion recognition; emotion representation; expert evaluation; face landmark detection; facial detection; facial emotion recognition; facial expression recognition; facial landmarks; feature extraction; feature selection; flight simulation; frustration; fully convolutional DenseNets; GSR; head-mounted display; homography matrix; human-computer interaction; in-ear EEG; information fusion; infrared thermal imaging; intensity of emotion recognition; interest; long short-term memory recurrent neural networks; long-term stress; machine learning; mental stress detection; multimodal sensing; multimodal sensors; musical genres; n/a; normalization; outpatient caring; overload; pain recognition; perceived stress scale; physiological sensing; physiological signal processing; physiological signals; psychophysiology; quantitative analysis; regression; respiration; road traffic; road types; sensor; sensor data analysis; signal analysis; signal processing; similarity measures; skip-connections; smart band; smart insoles; smart shoes; socially assistive robot; stress; stress detection; stress recognition; stress research; stress sensing; subject-dependent emotion recognition; subject-independent emotion recognition; thoracic electrical bioimpedance; time series analysis; transfer learning; underload; unobtrusive sensing; valence detection; Viola-Jones; virtual reality; wearable sensors; weighted loss
• Persona (resp. second.): Al-MachotFadi; MosaAhmad Haj; BouchachiaHamid; ChedjouJean Chamberlain; BagulaAntoine; KyamakyaKyandoghere
• Sommario/riassunto: 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.
• Titolo autorizzato: Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557346003321
• Lo trovi qui: Univ. Federico II