Deep Learning Techniques Applied To Affective Computing

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Deep Learning Techniques Applied To Affective Computing

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Author : Zhen Cui
language : en
Publisher: Frontiers Media SA
Release Date : 2023-06-14

Deep Learning Techniques Applied To Affective Computing written by Zhen Cui and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Science categories.

Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.

Applied Affective Computing

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Author : Leimin Tian
language : en
Publisher: Morgan & Claypool
Release Date : 2022-02-04

Applied Affective Computing written by Leimin Tian and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Computers categories.

Affective computing is a nascent field situated at the intersection of artificial intelligence with social and behavioral science. It studies how human emotions are perceived and expressed, which then informs the design of intelligent agents and systems that can either mimic this behavior to improve their intelligence or incorporate such knowledge to effectively understand and communicate with their human collaborators. Affective computing research has recently seen significant advances and is making a critical transformation from exploratory studies to real-world applications in the emerging research area known as applied affective computing. This book offers readers an overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. It provides in-depth case studies of applied affective computing in various domains, such as social robotics and mental well-being. It also addresses ethical concerns related to affective computing and how to prevent misuse of the technology in research and applications. Further, this book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered. For researchers and practitioners new to affective computing, this book will serve as an introduction to the field to help them in identifying new research topics or developing novel applications. For more experienced researchers and practitioners, the discussions in this book provide guidance for adopting a human-centered design and development approach to advance affective computing.

Multidisciplinary Applications Of Deep Learning Based Artificial Emotional Intelligence

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Author : Chowdhary, Chiranji Lal
language : en
Publisher: IGI Global
Release Date : 2022-10-21

Multidisciplinary Applications Of Deep Learning Based Artificial Emotional Intelligence written by Chowdhary, Chiranji Lal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-21 with Computers categories.

Emotional intelligence has emerged as an important area of research in the artificial intelligence field as it covers a wide range of real-life domains. Though machines may never need all the emotional skills that people need, there is evidence to suggest that machines require at least some of these skills to appear intelligent when interacting with people. To understand how deep learning-based emotional intelligence can be applied and utilized across industries, further study on its opportunities and future directions is required. Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence explores artificial intelligence applications, such as machine and deep learning, in emotional intelligence and examines their use towards attaining emotional intelligence acceleration and augmentation. It provides research on tools used to simplify and streamline the formation of deep learning for system architects and designers. Covering topics such as data analytics, deep learning, knowledge management, and virtual emotional intelligence, this reference work is ideal for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Machine And Deep Learning Techniques For Emotion Detection

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Author : Rai, Mritunjay
language : en
Publisher: IGI Global
Release Date : 2024-05-14

Machine And Deep Learning Techniques For Emotion Detection written by Rai, Mritunjay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Psychology categories.

Computer understanding of human emotions has become crucial and complex within the era of digital interaction and artificial intelligence. Emotion detection, a field within AI, holds promise for enhancing user experiences, personalizing services, and revolutionizing industries. However, navigating this landscape requires a deep understanding of machine and deep learning techniques and the interdisciplinary challenges accompanying them. Machine and Deep Learning Techniques for Emotion Detection offer a comprehensive solution to this pressing problem. Designed for academic scholars, practitioners, and students, it is a guiding light through the intricate terrain of emotion detection. By blending theoretical insights with practical implementations and real-world case studies, our book equips readers with the knowledge and tools needed to advance the frontier of emotion analysis using machine and deep learning methodologies.

Multi Agent Systems And Applications

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Author :
language : en
Publisher:
Release Date : 2005

Multi Agent Systems And Applications written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Artificial intelligence categories.

Emotion And Stress Recognition Related Sensors And Machine Learning Technologies

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Author : Kyandoghere Kyamakya
language : en
Publisher: MDPI
Release Date : 2021-09-01

Emotion And Stress Recognition Related Sensors And Machine Learning Technologies written by Kyandoghere Kyamakya and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Technology & Engineering categories.

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. This book, emerging from the Special Issue of the Sensors journal on “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies” emerges as a result of the crucial need for massive deployment of intelligent sociotechnical systems. Such technologies are being applied in assistive systems in different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies.

Proceedings

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Author :
language : en
Publisher:
Release Date : 2005

Proceedings written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Human-machine systems categories.

Smart Materials And Intelligent Systems Smis2010

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Author : H. Wang
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2010-10-28

Smart Materials And Intelligent Systems Smis2010 written by H. Wang and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-28 with Technology & Engineering categories.

Selected, peer reviewed papers from the International Conference on Smart Materials and Intelligent Systems (SMIS) 2010, December 17-20, 2010, Chongqing, China

Emotion Detection Using Deep Learning Techniques

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Author : Syyada Shumaila Khurshid
language : en
Publisher: Independently Published
Release Date : 2024-10-19

Emotion Detection Using Deep Learning Techniques written by Syyada Shumaila Khurshid and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-19 with Computers categories.

Determining human emotions from photographs is a difficult but important challenge for social communication. Emotion detection using traditional approaches is typically inefficient and inaccurate. In this study, we investigate how convolutional neural networks (CNNs), a type of deep learning technique, may improve the ability to identify emotions from facial expressions. In order to increase CNN efficacy, we test several preprocessing methods and refine CNN designs to identify eight fundamental emotions. Our goal is to improve human emotion recognition and classification through deep learning, so that computers can react to human emotions and behaviors more precisely. The research dataset consists of roughly 32,290 photos with various expressions on their faces. Our approach includes preprocessing processes like feature extraction and noise reduction to improve image quality. To reliably classify facial expressions, we present an enhanced CNN (ECNN) technique that is in line with the Facial Action Coding System (FACS). We test our ECNN model empirically and compare its performance to that of conventional CNNs and support vector machines (SVMs). The results show that our ECNN methodology achieves higher accuracy rates in emotion categorization than previous methods. We show notable gains in computing efficiency and classification performance by utilizing deep learning techniques. Our research advances face expression recognition technology, which has ramifications for a number of fields including social robots, affective computing, and human-computer interaction.

Advances In Artificial Intelligence

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Author :
language : en
Publisher:
Release Date : 2004

Advances In Artificial Intelligence written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Artificial intelligence categories.