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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 online resource (550 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
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 emotion 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 regression 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 thoracic electrical bioimpedance time series analysis transfer learning underload unobtrusive sensing valence detection Viola-Jones virtual reality wearable sensors weighted loss |
| 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 online resource (114 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general Technology, engineering, agriculture |
| Soggetto non controllato |
artificial neural networks
avocado botanical origin classification consumer acceptance prediction consumer science cultivars data fusion deep learning emotion recognition facial expression recognition flavor lexicon galvanic skin response long short-term memory machine learning machine learning modeling n/a natural language processing near infra-red spectroscopy neural networks physicochemical measurements physicochemical parameters preference mapping sensory sensory analysis sensory descriptive analysis sensory evaluation sensory science unifloral honeys weather wine quality |
| 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 online resource (382 p.) |
| Soggetto topico |
History of engineering and technology
Technology: general issues |
| Soggetto non controllato |
3D human mesh reconstruction
3D human pose estimation 3D multi-person pose estimation absolute poses action units aggregation function anomaly detection artifact classification artifact detection artificial intelligence assessment Azure Kinect balance Berg Balance Scale BILSTM biometrics camera-centric coordinates computer vision convolutional neural networks COVID-19 cyber-physical systems data augmentation deep learning deep neural network deep-learning development diagnosis elderly EMG F-Formation facial expression recognition facial landmarks fall risk detection features fusion features selection FFNN FMCW fuzzy inference gait analysis gait parameters gait recognition gap filling generalization graph convolutional networks grey wolf optimization GRU human action recognition human motion analysis human motion modelling human tracking Kinect v2 kinematics knowledge measure LSTM machine learning markerless markerless motion capture MFCC modular sensing unit motion capture movement tracking n/a neural networks optical sensing principle particle swarm optimization pattern recognition plantar pressure measurement pose estimation posture detection precedence indicator recognition reconstruction regularized discriminant analysis RGB-D sensors robot rule induction skeletal data socially occupied space telemedicine time series classification vital sign whale optimization algorithm XGBoost Zed 2i |
| 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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