Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

• Weitere Verfasser: Kyamakya, Kyandoghere (Herausgegeben von), Al-Machot, Fadi (Herausgegeben von), Mosa, Ahmad Haj (Herausgegeben von), Bouchachia, Hamid (Herausgegeben von), Chedjou, Jean Chamberlain (Herausgegeben von), Bagula, Antoine (Herausgegeben von)
• Format: Elektronisch Buchkapitel
• Sprache: Englisch
• Veröffentlicht: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Schlagworte: 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-Zugang: 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 c...