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Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
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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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Keywords
- 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
- Émotion
- 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
- régression
- 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
- Technology, engineering, agriculture
- Technology: general issues
- thoracic electrical bioimpedance
- Time Series Analysis
- transfer learning
- underload
- unobtrusive sensing
- valence detection
- Viola-Jones
- Virtual reality
- Wearable sensors
- weighted loss
Links
DOI: 10.3390/books978-3-0365-1139-9Editions