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Focusing on the vision-based and sensor-based recognition and analysis of human activity and behavior, this book gathers extended versions of selected papers presented at the International Conference on Activity and Behavior Computing (ABC 2020), held in Kitakyushu, Japan on August 26 – 29, 2020. The respective chapters cover action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition in both the sensor-based and vision-based domains, making it a unique guide to the field.
This book presents the best-selected research papers presented at the 3rd International Conference on Activity and Behavior Computing (ABC 2021), during 20–22 October 2021. The book includes works related to the field of vision- and sensor-based human action or activity and behavior analysis and recognition. It covers human activity recognition (HAR), action understanding, gait analysis, gesture recognition, behavior analysis, emotion, and affective computing, and related areas. The book addresses various challenges and aspects of human activity recognition—both in sensor-based and vision-based domains. It can be considered as an excellent treasury related to the human activity and behavior computing.
Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects. The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on two main subject areas: Movement and Sensors, and Sports Activity Analysis. The editors are experts in these arenas, and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.
'Behavior' is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence. With contributions from leading researchers in this emerging field Behavior Computing: Modeling, Analysis, Mining and Decision includes chapters on: representation and modeling behaviors; behavior ontology; behaviour analysis; behaviour pattern mining; clustering complex behaviors; classification of complex behaviors; behaviour impact analysis; social behaviour analysis; organizational behaviour analysis; and behaviour computing applications. Behavior Computing: Modeling, Analysis, Mining and Decision provides a dedicated source of reference for the theory and applications of behavior informatics and behavior computing. Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable.
'Behavior' is an increasingly important concept in the scientific, societal, economic, cultural, political, military, living and virtual worlds. Behavior computing, or behavior informatics, consists of methodologies, techniques and practical tools for examining and interpreting behaviours in these various worlds. Behavior computing contributes to the in-depth understanding, discovery, applications and management of behavior intelligence. With contributions from leading researchers in this emerging field Behavior Computing: Modeling, Analysis, Mining and Decision includes chapters on: representation and modeling behaviors; behavior ontology; behaviour analysis; behaviour pattern mining; clustering complex behaviors; classification of complex behaviors; behaviour impact analysis; social behaviour analysis; organizational behaviour analysis; and behaviour computing applications. Behavior Computing: Modeling, Analysis, Mining and Decision provides a dedicated source of reference for the theory and applications of behavior informatics and behavior computing. Researchers, research students and practitioners in behavior studies, including computer science, behavioral science, and social science communities will find this state of the art volume invaluable.
nd Welcome to the proceedings of PERVASIVE 2004, the 2 International C- ference on Pervasive Computing and the premier forum for the presentation and appraisal of the most recent and most advanced research results in all - undational and applied areas of pervasive and ubiquitous computing. Consi- ring the half-life period of technologies and knowledge this community is facing, PERVASIVE is one of the most vibrant, dynamic, and evolutionary among the computer-science-related symposia and conferences. The research challenges, e?orts, and contributions in pervasive computing have experienced a breathtaking acceleration over the past couple of years, mostly due to technological progress, growth, and a shift of paradigms in c- puter science in general. As for technological advances, a vast manifold of tiny, embedded, and autonomous computing and communication systems have st- ted to create and populate a pervasive and ubiquitous computing landscape, characterized by paradigms like autonomy, context-awareness, spontaneous - teraction, seamless integration, self-organization, ad hoc networking, invisible services, smart artifacts, and everywhere interfaces. The maturing of wireless networking, miniaturized information-processing possibilities induced by novel microprocessor technologies, low-power storage systems, smart materials, and technologies for motors, controllers, sensors, and actuators envision a future computing scenario in which almost every object in our everyday environment will be equipped with embedded processors, wireless communication facilities, and embedded software to perceive, perform, and control a multitude of tasks and functions.
Brain and Behavior Computing offers insights into the functions of the human brain. This book provides an emphasis on brain and behavior computing with different modalities available such as signal processing, image processing, data sciences, statistics further it includes fundamental, mathematical model, algorithms, case studies, and future research scopes. It further illustrates brain signal sources and how the brain signal can process, manipulate, and transform in different domains allowing researchers and professionals to extract information about the physiological condition of the brain. Emphasizes real challenges in brain signal processing for a variety of applications for analysis, classification, and clustering. Discusses data sciences and its applications in brain computing visualization. Covers all the most recent tools for analysing the brain and it’s working. Describes brain modeling and all possible machine learning methods and their uses. Augments the use of data mining and machine learning to brain computer interface (BCI) devices. Includes case studies and actual simulation examples. This book is aimed at researchers, professionals, and graduate students in image processing and computer vision, biomedical engineering, signal processing, and brain and behavior computing.
The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).
This book provides a broad survey of advanced pattern recognition techniques for human behavior analysis. Clearly structured, the book begins with concise coverage of the major concepts, before introducing the most frequently used techniques and algorithms in detail, and then discussing examples of real applications. Features: contains contributions from an international selection of experts in the field; presents a thorough introduction to the fundamental topics of human behavior analysis; investigates methods for activity recognition, including gait and posture analysis, hand gesture analysis, and semantics of human behavior in image sequences; provides an accessible psychological treatise on social signals for the analysis of social behaviors; discusses voice and speech analysis, combined audiovisual cues, and social interactions and group dynamics; examines applications in different research fields; each chapter concludes with review questions, a summary of the topics covered, and a glossary.
Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.