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 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.

Human conversational partners are able, at least to a certain extent, to detect the speaker’s or listener’s emotional state and may attempt to respond to it accordingly. When instead one of the interlocutors is a computer a number of questions arise, such as the following: To what extent are dialogue systems able to simulate such behaviors? Can we learn the mechanisms of emotional be- viors from observing and analyzing the behavior of human speakers? How can emotionsbeautomaticallyrecognizedfromauser’smimics,gesturesandspeech? What possibilities does a dialogue system have to express emotions itself? And, very importantly, would emotional system behavior be desirable at all? Given the state of ongoing research into incorporating emotions in dialogue systems we found it timely to organize a Tutorial and Research Workshop on A?ectiveDialogueSystems(ADS2004)atKlosterIrseein GermanyduringJune 14–16, 2004. After two successful ISCA Tutorial and Research Workshops on Multimodal Dialogue Systems at the same location in 1999 and 2002, we felt that a workshop focusing on the role of a?ect in dialogue would be a valuable continuation of the workshop series. Due to its interdisciplinary nature, the workshop attracted submissions from researchers with very di?erent backgrounds and from many di?erent research areas, working on, for example, dialogue processing, speech recognition, speech synthesis, embodied conversational agents, computer graphics, animation, user modelling, tutoring systems, cognitive systems, and human-computer inter- tion.

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems.

Traditional patient care and treatment approaches often lack the personalized and interactive elements necessary for effective healthcare delivery. This means that the healthcare industry must find innovative solutions to improve patient outcomes, enhance rehabilitation processes, and optimize resource utilization. There is a gap between the traditional approach and the need for innovation that highlights the importance of a comprehensive understanding of emerging technologies, including Kinect Sensor technology, and the potential to transform healthcare practices with this tech. Revolutionizing Healthcare Treatment With Sensor Technology addresses this critical need by thoroughly exploring how Kinect Sensor technology can revolutionize patient care and treatment methodologies. By repurposing and customizing Kinect Sensor for healthcare applications, this book showcases how depth-sensing cameras, infrared sensors, and advanced motion tracking can capture and interpret real-time patient movements and interactions. This book is ideal for healthcare professionals, hospital administrators, researchers, patients, caregivers, and healthcare technology developers seeking to leverage Kinect Sensor technology for enhanced healthcare delivery. Through detailed case studies and practical examples, experts can learn how to integrate Kinect Sensor into various medical settings to gain valuable insights into patients' physical capabilities, monitor their progress, and create personalized treatment plans.

This book reports on the theoretical foundations, fundamental applications and latest advances in various aspects of connected services for health information systems. The twelve chapters highlight state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of multisensory systems and tools for health management in smart city ecosystems. They exploit technologies like deep learning, artificial intelligence, augmented and virtual reality, cyber physical systems and sensor networks. Presenting the latest developments, identifying remaining challenges, and outlining future research directions for sensing, computing, communications and security aspects of connected health systems, the book will mainly appeal to academic and industrial researchers in the areas of health information systems, smart cities, and augmented reality.