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 electronic resource (550 p.)
• Soggetto topico: Technology: general issues
• Soggetto non controllato: 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
• 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