This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily human behavior, and fitness monitoring. In addition, the complexities, challenges, and design issues involved in data collection, processing, and other fundamental stages along with datasets, methods, etc., are discussed in detail. The book offers a valuable resource for readers in the fields of pattern recognition, human–computer interaction, and the Internet of Things.

This book consists of a number of chapters addressing different aspects of activity recognition, roughly in three main categories of topics. The first topic will be focused on activity modeling, representation and reasoning using mathematical models, knowledge representation formalisms and AI techniques. The second topic will concentrate on activity recognition methods and algorithms. Apart from traditional methods based on data mining and machine learning, we are particularly interested in novel approaches, such as the ontology-based approach, that facilitate data integration, sharing and automatic/automated processing. In the third topic we intend to cover novel architectures and frameworks for activity recognition, which are scalable and applicable to large scale distributed dynamic environments. In addition, this topic will also include the underpinning technological infrastructure, i.e. tools and APIs, that supports function/capability sharing and reuse, and rapid development and deployment of technological solutions. The fourth category of topic will be dedicated to representative applications of activity recognition in intelligent environments, which address the life cycle of activity recognition and their use for novel functions of the end-user systems with comprehensive implementation, prototyping and evaluation. This will include a wide range of application scenarios, such as smart homes, intelligent conference venues and cars.

This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.

The book first defines the problems, various concepts and notions related to activity recognition, and introduces the fundamental rationale and state-of-the-art methodologies and approaches. It then describes the use of artificial intelligence techniques and advanced knowledge technologies for the modelling and lifecycle analysis of human activities and behaviours based on real-time sensing observations from sensor networks and the Internet of Things. It also covers inference and decision-support methods and mechanisms, as well as personalization and adaptation techniques, which are required for emerging smart human-machine pervasive systems, such as self-management and assistive technologies in smart healthcare. Each chapter includes theoretical background, technological underpinnings and practical implementation, and step-by-step information on how to address and solve specific problems in topical areas. This monograph can be used as a textbook for postgraduate and PhD students on courses such as computer systems, pervasive computing, data analytics and digital health. It is also a valuable research reference resource for postdoctoral candidates and academics in relevant research and application domains, such as data analytics, smart cities, smart energy, and smart healthcare, to name but a few. Moreover, it offers smart technology and application developers practical insights into the use of activity recognition and behaviour analysis in state-of-the-art cyber-physical systems. Lastly, it provides healthcare solution developers and providers with information about the opportunities and possible innovative solutions for personalized healthcare and stratified medicine.

The success of Activity Recognition (AR) methodology largely depends on the availability of labeled training samples and adaptability of activity recognition models in cross-domains such as diverse users, heterogeneous devices, and different smart environments. The availability of new era of Internet-of-Things (IoT) devices ranging from smartphones, smartwatches, micro-radars, Amazon Echo in users everyday environments ease the recognition of human activities, behaviors, and occupancy. Nevertheless, the variabilities across emerging sensors, heterogeneities in consumer devices, and inherent variations in users' activities hinder the design and development of scalable activity recognition models. Motivated by this, in this thesis, we investigate the problem of making human activity recognition scalable-i.e., allowing AR classifiers trained in one context to be readily adapted to a different contextual domain. To allow such adaptation without requiring the onerous step of collecting large volumes of labeled training data, we proposed a transfer learning model that is specifically tuned to the properties of convolutional neural networks (CNNs). We designed different variants of this Heterogeneous Deep Convolutional Neural Network (HDCNN) model that help to automatically adapt and learn the model across different domains, such as different users, device-types, device-instances in presence of completely or partially alike activities in source and target. We also extended the above cross-domain activity recognition models to learn the unseen activities using the deep features transfer learning technique while aggregating the domain knowledge from both the source and target domains. Evaluation on real world datasets attested that our proposed cross-domain activity recognition models are able to achieve high accuracy even without any labeled training data in the target domain, and often offer higher accuracy (compared to shallow and deep classifiers) even with a modest amount of labeled training data.

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.