Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

• Other Authors: Kyamakya, Kyandoghere (Editor), Al-Machot, Fadi (Editor), Mosa, Ahmad Haj (Editor), Bouchachia, Hamid (Editor), Chedjou, Jean Chamberlain (Editor), Bagula, Antoine (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Subjects: 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.