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Book
Intelligent Sensors for Human Motion Analysis
Authors: --- --- ---
ISBN: 3036550747 3036550739 Year: 2022 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems.

Keywords

Technology: general issues --- History of engineering & technology --- 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 --- n/a


Book
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
Authors: --- --- --- --- --- et al.
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

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.

Keywords

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


Book
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
Authors: --- --- --- --- --- et al.
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

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.

Keywords

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


Book
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
Authors: --- --- --- --- --- et al.
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

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.

Keywords

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 --- 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

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