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.

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.

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.

This volume provides alternatives for tackling existing empirical, methodological, and analytical challenges. It does so by providing a broad overview of less established, as well as emerging methods, which are of great relevance for current research on professional learning and development. As such, it offers a comprehensive collection of state-of-the-art methodologies and future directions within the workplace learning and professional development research. By describing these novel approaches and providing empirical illustrations, the book promotes innovative methodologies for investigating professional learning and development. It also supports scholars to understand upcoming empirical research and methods and encourages novice as well as established researchers to adopt new empirical strategies beyond traditional ones that have the potential to enrich a better understanding of professional learning and development.

This book contains the best papers of the Second International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2009), organized by the Institute for Systems and Technologies of Information Control and Communi- tion (INSTICC), technically co-sponsored by the IEEE Engineering in Medicine and Biology Society (EMB), IEEE Circuits and Systems Society (CAS) and the Workflow Management Coalition (WfMC), in cooperation with AAAI and ACM SIGART. The purpose of the International Joint Conference on Biomedical Engineering S- tems and Technologies is to bring together researchers and practitioners, including engineers, biologists, health professionals and informatics/computer scientists, int- ested in both theoretical advances and applications of information systems, artificial intelligence, signal processing, electronics and other engineering tools in knowledge areas related to biology and medicine. BIOSTEC is composed of three co-located conferences; each specializes in one of the aforementioned main knowledge areas, namely: • BIODEVICES (International Conference on Biomedical Electronics and - vices) focuses on aspects related to electronics and mechanical engineering, - pecially equipment and materials inspired from biological systems and/or - dressing biological requirements. Monitoring devices, instrumentation sensors and systems, biorobotics, micro-nanotechnologies and biomaterials are some of the technologies addressed at this conference.

The core message of this book is: computer games best realise affective interaction. This book brings together contributions from specialists in affective computing, game studies, game artificial intelligence, user experience research, sensor technology, multi-modal interfaces and psychology that will advance the state-of-the-art in player experience research; affect modelling, induction, and sensing; affect-driven game adaptation and game-based learning and assessment. In 3 parts the books covers Theory, Emotion Modelling and Affect-Driven Adaptation, and Applications. This book will be of interest to researchers and scholars in the fields of game research, affective computing, human computer interaction, and artificial intelligence.