Smartphone Based Human Activity Recognition

Author: Jorge Luis Reyes Ortiz
Publisher: Springer
ISBN: 3319142747
Size: 75.50 MB
Format: PDF
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Smartphone Based Human Activity Recognition from the Author: Jorge Luis Reyes Ortiz. The book reports on the author’s original work to address the use of today’s state-of-the-art smartphones for human physical activity recognition. By exploiting the sensing, computing and communication capabilities currently available in these devices, the author developed a novel smartphone-based activity-recognition system, which takes into consideration all aspects of online human activity recognition, from experimental data collection, to machine learning algorithms and hardware implementation. The book also discusses and describes solutions to some of the challenges that arose during the development of this approach, such as real-time operation, high accuracy, low battery consumption and unobtrusiveness. It clearly shows that it is possible to perform real-time recognition of activities with high accuracy using current smartphone technologies. As well as a detailed description of the methods, this book also provides readers with a comprehensive review of the fundamental concepts in human activity recognition. It also gives an accurate analysis of the most influential works in the field and discusses them in detail. This thesis was supervised by both the Universitat Politècnica de Catalunya (primary institution) and University of Genoa (secondary institution) as part of the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments.

Human Activity Recognition

Author: Miguel A. Labrador
Publisher: CRC Press
ISBN: 1466588284
Size: 45.81 MB
Format: PDF, Mobi
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Human Activity Recognition from the Author: Miguel A. Labrador. Learn How to Design and Implement HAR Systems The pervasiveness and range of capabilities of today’s mobile devices have enabled a wide spectrum of mobile applications that are transforming our daily lives, from smartphones equipped with GPS to integrated mobile sensors that acquire physiological data. Human Activity Recognition: Using Wearable Sensors and Smartphones focuses on the automatic identification of human activities from pervasive wearable sensors—a crucial component for health monitoring and also applicable to other areas, such as entertainment and tactical operations. Developed from the authors’ nearly four years of rigorous research in the field, the book covers the theory, fundamentals, and applications of human activity recognition (HAR). The authors examine how machine learning and pattern recognition tools help determine a user’s activity during a certain period of time. They propose two systems for performing HAR: Centinela, an offline server-oriented HAR system, and Vigilante, a completely mobile real-time activity recognition system. The book also provides a practical guide to the development of activity recognition applications in the Android framework.

Human Activity Recognition And Prediction

Author: Yun Fu
Publisher: Springer
ISBN: 3319270044
Size: 16.72 MB
Format: PDF, ePub, Mobi
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Human Activity Recognition And Prediction from the Author: Yun Fu. 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.

A Unified Framework For Human Activity Detection And Recognition For Video Surveillance Using Dezert Smarandache Theory

Author: Srilatha V.
Publisher: Infinite Study
Size: 37.71 MB
Format: PDF, Mobi
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A Unified Framework For Human Activity Detection And Recognition For Video Surveillance Using Dezert Smarandache Theory from the Author: Srilatha V.. Trustworthy contextual data of human action recognition of remotely monitored person who requires medical care should be generated to avoid hazardous situation and also to provide ubiquitous services in home-based care. It is difficult for numerous reasons. At first level, the data obtained from heterogeneous source have different level of uncertainty. Second level generated information can be corrupted due to simultaneous operations. In this paper human action recognition can be done based on two different modality consisting of fully featured camera and wearable sensor.

Human Activity Recognition In Video

Author: Ross Messing
Size: 73.17 MB
Format: PDF, ePub
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Human Activity Recognition In Video from the Author: Ross Messing. "This thesis explores the problem of recognizing complex human activities involving the manipulation of objects in high resolution video. Inspired by human psychophysical performance, I develop and evaluate an activity recognition feature derived from the velocity histories of tracked keypoints. These features have a much greater spatial and temporal range than existing video features. I show that a generative mixture model using these features performs comparably to local spatio-temporal features on the KTH activity recognition dataset. I additionally introduce and explore a new activity recognition dataset of activities of daily living (URADL), containing high resolution video sequences of complex activities. I demonstrate the superior performance of my velocity history feature on this dataset, and explore ways in which it can be extended. I investigate the value of a more sophisticated latent velocity model for velocity histories. I explore the addition of contextual semantic information to the model, whether fully automatic or derived from supervision, and provide a sketch for the inclusion of this information in any feature-based generative model for activity recognition or time series data. This approach performs comparably to established methods on the KTH dataset, and significantly outperforms local spatio-temporal features on the challenging new URADL dataset. I further develop another new dataset, URADL2, and explore transferring knowledge between related video activity recognition domains. Using a straightforward feature-expansion transfer learning technique, I show improved performance on one dataset using activity models transferred from the other dataset"--Leaves iv-v.

Human Activity Recognition Using Limb Component Extraction

Author: Jamie Lynn Boeheim
ISBN: 9781243445193
Size: 78.99 MB
Format: PDF
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Human Activity Recognition Using Limb Component Extraction from the Author: Jamie Lynn Boeheim. Interest in the field of human activity recognition has existed for quite sometime, but has gained popularity in recent years for use in many areas of application. In the security industry, suspicious activities could be detected in high-profile areas. In the medical industry, systems could be trained to detect patterns of motion indicating distress or to detect a lack of motion if a person had fallen and was unable to move. However, algorithms with reliable accuracy are difficult to implement in a real-time environment due to computational complexity. This thesis developed a new way of extracting and using data from a human figure in a video frame to determine what type of activity the subject is performing. Following background subtraction, a thinning algorithm operating on the silhouette offered a more robust limb extraction method, while a six-segment representation of the human figure offered more accuracy in deriving limb parameters, or components, such as distance from torso, and angle of displacement from the vertical axis. Neural networks or nearest neighbor classifiers used the limb components to identify a number of activities, such as walking, running, waving and jumping. This entire human activity recognition system was tested with both a MATLAB implementation (non real-time) and a C++ implementation in OpenCV (real-time). The algorithm achieved 96% classification accuracy in video feeds, which is only slightly lower than that of intensive, non real-time systems.

Wearable Human Activity Recognition Systems

Author: Alireza Ameri-Daragheh
ISBN: 9781321952490
Size: 28.50 MB
Format: PDF, Docs
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Wearable Human Activity Recognition Systems from the Author: Alireza Ameri-Daragheh. Abstract: In this thesis, we focused on designing wearable human activity recognition (WHAR) systems. As the first step, we conducted a thorough research over the publications during the recent ten years in this area. Then, we proposed an all-purpose architecture for designing the software of WHAR systems. Afterwards, among various applications of these wearable systems, we decided to work on wearable virtual fitness coach device which can recognize various types and intensities of warm-up exercises that an athlete performs. We first proposed a basic hardware platform for implementing the WHAR software. Afterwards, the software design was done in two phases. In the first phase, we focused on four simple activities to be recognized by the wearable device. We used Weka machine learning tool to build a mathematical model which could recognize the four activities with the accuracy of 99.32%. Moreover, we proposed an algorithm to measure the intensity of the activities with the accuracy of 93%. In the second phase, we focused on eight complex warm-up exercises. After building the mathematical model, the WHAR system could recognize the eight activities with the accuracy of 95.60%.

Recognition Of Humans And Their Activities Using Video

Author: Rama Chellappa
Publisher: Morgan & Claypool Publishers
ISBN: 159829007X
Size: 32.24 MB
Format: PDF, Docs
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Recognition Of Humans And Their Activities Using Video from the Author: Rama Chellappa. The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusion of different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.