Download Free Video Modelling And Behaviour Analysis Book in PDF and EPUB Free Download. You can read online Video Modelling And Behaviour Analysis and write the review.

Video modelling is a behaviour modification technique using videotaped scenarios for the child to observe, concentrating the focus of attention and creating an effective stimulus for learning. This book introduces the technique. Illustrative case examples are supported by detailed diagrams and photographs, with clear, accessible explanations.
Applied Behaviour Analysis (ABA) is a successful educational method for developing social and communication skills in children with autism. The use of video modelling in ABA programmes has demonstrated great effectiveness in teaching behavioural skills to autistic children, and this book explains how and why. Video modelling is an easy-to-use behaviour modification technique that uses videotaped rather than 'live' scenarios for the child to observe, concentrating the focus of attention for the child with autism and creating a highly effective stimulus for learning. Video Modelling and Behaviour Analysis provides a practical introduction to the technique, its objectives, strategies for use and evidence of its success. Illustrative case examples are supported by detailed diagrams and photographs, with clear, accessible explanations. Video Modelling and Behaviour Analysis will be a welcome addition to the practical literature on autism interventions for parents of autistic children and the professionals working with them.
This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes. Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed. The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.
Discusses, in handbook form, the use of videotape for studying and influencing human behavior. Provides a comprehensive review of the scientific and therapeutic ideas connected with the video medium. Surveys existing and potential applications and explores video as a direct change agent. Includes a case study and discusses theories behind the use of video. Evaluates the state-of-the-art and future directions.
This text details the principles of behavior analysis (as well as the experimental evidence underlying the principles) and examines the factors that make behavioral principles effective.
This book provides step-by-step guidance for using innovative video modeling techniques to support the development of young children with autism spectrum disorders. It shows how to film personalized videos that highlight the exact skill that is being taught and how to incorporate these videos into the child's daily routine to encourage learning.
This hands-on guide to the use of video in the behavioral sciences identifies and provides detailed descriptions of both current and potential uses of the medium. Both authoritative and practical, it supplements every use described in Part I with contributions by a team of international experts, illustrating applications for each purpose in Part II. Covers topics and applications in interactive video, video for assessment and documentation, analysis of facial expression and emotion, video vignettes, video use at the community level and much more.
'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 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.
Volume 1 demonstrates the Cool versus Not Cool strategy. This is one of Autism Partnership's most often used strategies for teaching students foundational as well as advanced social skills. Essentially, the strategy teaches students to understand the difference between behaviors that are socially appropriate (cool) and those that are inappropriate (not cool).