Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
Basel, Switzerland: MDPI - Multidisciplinary Digital Publishing Institute, 2021
Online
E-Book
- 550
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.
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Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
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Autor/in / Beteiligte Person: | Kyamakya, Kyandoghere ; Al-Machot, Fadi ; Mosa, Ahmad Haj ; Bouchachia, Hamid ; Chedjou, Jean Chamberlain ; Bagula, Antoine |
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Veröffentlichung: | Basel, Switzerland: MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
Medientyp: | E-Book |
Umfang: | 550 |
ISBN: | 978-3-0365-1138-2 (print) ; 978-3-0365-1139-9 (print) |
DOI: | 10.3390/books978-3-0365-1139-9 |
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