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Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
| Emotion and Stress Recognition Related Sensors and Machine Learning Technologies |
| Autore | Kyamakya Kyandoghere |
| Pubbl/distr/stampa | 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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557346003321 |
Kyamakya Kyandoghere
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment
| Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment |
| Autore | Fuentes Sigfredo |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (114 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Technology, engineering, agriculture |
| Soggetto non controllato |
sensory
physicochemical measurements artificial neural networks near infra-red spectroscopy wine quality machine learning modeling weather consumer acceptance prediction data fusion emotion recognition facial expression recognition galvanic skin response machine learning neural networks sensory analysis avocado cultivars preference mapping sensory evaluation sensory descriptive analysis consumer science unifloral honeys botanical origin physicochemical parameters classification natural language processing deep learning sensory science flavor lexicon long short-term memory |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566478503321 |
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Fuentes Sigfredo
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent Sensors for Human Motion Analysis
| Intelligent Sensors for Human Motion Analysis |
| Autore | Krzeszowski Tomasz |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (382 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
gait recognition
biometrics regularized discriminant analysis particle swarm optimization grey wolf optimization whale optimization algorithm FMCW vital sign XGBoost MFCC COVID-19 3D human pose estimation deep learning generalization optical sensing principle modular sensing unit plantar pressure measurement gait parameters 3D human mesh reconstruction deep neural network motion capture neural networks reconstruction gap filling FFNN LSTM BILSTM GRU pose estimation movement tracking computer vision artificial intelligence markerless motion capture assessment kinematics development machine learning human action recognition features fusion features selection recognition fall risk detection balance Berg Balance Scale human tracking elderly telemedicine diagnosis precedence indicator knowledge measure fuzzy inference rule induction posture detection aggregation function markerless human motion analysis gait analysis data augmentation skeletal data time series classification EMG pattern recognition robot cyber-physical systems RGB-D sensors human motion modelling F-Formation Kinect v2 Azure Kinect Zed 2i socially occupied space facial expression recognition facial landmarks action units convolutional neural networks graph convolutional networks artifact classification artifact detection anomaly detection 3D multi-person pose estimation absolute poses camera-centric coordinates deep-learning |
| ISBN | 3-0365-5074-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469003321 |
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Krzeszowski Tomasz
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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