Download or read book Towards Robust Realtime Human Activity Recognition Using Wearable Sensors written by Delaram Yazdansepas and published by . This book was released on 2017 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the proliferation of smartphones and fitness bands that have various sensors such as accelerometers, wearable sensor-based Human Activity Recognition (HAR) systems have gained wide popularity and researchers have proposed numerous techniques for recognition of these activities. Human activity recognition has many applications particularly in health care, cognitive assistance, city planning, indoor localization and tracking, and human-computer interaction. Although there has been some progress, a practical robust HAR system remains elusive because the collected data are affected by several factors such as noise, data alignment, and other constraints. In addition, the variability in the sensing equipment and their displacement is a practical challenge for implementing HAR in real-world applications. This dissertation explores the twin problems of making wearable sensor-based HAR systems robust and real time. Towards enhancing the robustness of ML-based HAR systems, we adopt feature selection methods on time and frequency domain features and apply classifiers for evaluating the recognition performance. We show the effect of different feature sets on each of the classifiers and further demonstrate in our results the impact of decreasing the size of the training set on the accuracy of the classifiers. Towards building an Online HAR system, this thesis explores the concept of Shapelets to avoid complex feature extraction. We propose a procedure to find the most representative shapelet for each activity class based on time series distance metrics and dynamic time warping. Furthermore, we generate a personalized shapelet library database driven from users' activity time series. We evaluate the proposed algorithm and techniques using a dataset comprised of accelerometer readings of 77 individuals performing various activities such as walking/jogging on treadmill, walking on different surfaces, climbing stairs, and non-ambulatory activities. Our experiments demonstrate that by using selected features from the time and frequency domain, we can achieve higher accuracy rates if we limit the training and testing sets to specific age groups. Furthermore, while we mainly use a single hip-worn accelerometer sensor as our sensing device, we show our method could support any wearable accelerometer sensor.

Download or read book Human Activity Recognition written by Miguel A. Labrador and published by CRC Press. This book was released on 2013-12-05 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Vision Based Human Activity Recognition written by Zhongxu Hu and published by Springer Nature. This book was released on 2022-04-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human–machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.

Download or read book Smartphone Based Human Activity Recognition written by Jorge Luis Reyes Ortiz and published by Springer. This book was released on 2015-01-14 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning for Human Activity Recognition written by Xiaoli Li and published by Springer Nature. This book was released on 2021-02-17 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.

Download or read book Human Activity Sensing written by Nobuo Kawaguchi and published by Springer Nature. This book was released on 2019-09-09 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.

Download or read book A Case Study on Robustness of Dynamic Time Warping for Activity Recognition Using Wearable Computers written by Nimish Rajiv Kale and published by . This book was released on 2012 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe a body sensor system that detects human activities in real-time. The system consists of wearable computers known as sensor nodes (motes) that can sense information, process them and transmit the results to a Personal Device like Smart phone, PDA or Personal Computer. The motes are attached to different parts of the human body, namely waist and right thigh. Daily living activity monitoring is important in improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing for time-series pattern matching because of its robustness to variations in time domain and speed as opposed to other template matching methods such as Euclidean Distance. Despite of this flexibility, for the application of activity recognition, DTW can only find the similarity between template of a movement and the incoming samples, when the location and orientation of sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to false classifications. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand. To measure this performance of DTW, we need infinite closely spaced sensors which are impractical. To deal with this problem, we use the marker based optical motion capture system and generate inertial sensor data for different location and orientation on the body. We study the performance of the DTW under these conditions and determine the worst-case sensor location variations, the algorithm can accommodate.

Download or read book Robust Human Activity Recognition Using Smartwatches and Smartphones written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Smart user devices are becoming increasingly ubiquitous and useful for detecting the user's context and his/her current activity. This work analyzes and proposes several techniques to improve the robustness of a Human Activity Recognition (HAR) system that uses accelerometer signals from different smartwatches and smartphones. This analysis reveals some of the challenges associated with both device heterogeneity and the different use of smartwatches compared to smartphones. When using smartwatches to recognize whole body activities, the arm movements introduce additional variability giving rise to a significant degradation in HAR. In this analysis, we describe and evaluate several techniques which successfully address these challenges when using smartwatches and when training and testing with different devices and/or users. Highlights: Study of the differences between signals from smartphones and smartwatches. Increasing robustness for Human Activity Recognition (HAR). Feature extraction analysis: discrimination and robustness against degradation. Activity time modeling using Hidden Markov Models and Recurrent Neural Networks. Comparison of traditional and deep machine learning strategies.

Download or read book Sensor Based Human Activity Recognition for Assistive Health Technologies written by Muhammad Adeel Nisar and published by Logos Verlag Berlin GmbH. This book was released on 2023-02-20 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: The average age of people has increased due to advances in health sciences, which has led to an increase in the elderly population. This is positive news, but it also raises questions about the quality of independent living for older people. Clinicians use Activities of Daily Living (ADLs) to assess older people's ability to live independently. In recent years, portable computing devices have become more present in our daily lives. Therefore, a software system that can detect ADLs based on sensor data collected from wearable devices is beneficial for detecting health problems and supporting health care. In this context, this book presents several machine learning-based approaches for human activity recognition (HAR) using time-series data collected by wearable sensors in the home environment. In the first part of the book, machine learning-based approaches for atomic activity recognition are presented, which are relatively simple and short-term activities. In the second part, the algorithms for detecting long-term and complex ADLs are presented. In this part, a two-stage recognition framework is also presented, as well as an online recognition system for continuous monitoring of HAR. In the third and final part, a novel approach is proposed that not only solves the problem of data scarcity but also improves the performance of HAR by implementing multitask learning-based methods. The proposed approach simultaneously trains the models of short- and long-term activities, regardless of their temporal scale. The results show that the proposed approach improves classification performance compared to single-task learning.

Download or read book Human Activity Recognition Using Wearable Sensors written by Jamie O'Halloran and published by Eliva Press. This book was released on 2020-04-04 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technological advancements in healthcare can contribute unquestionably in reducing healthcare strains by ensuring clinicians, doctors and other medical staff operate and conduct their daily activities more efficiently in the hospital vicinity. Since the turn of the 21st century, Human Activity Recognition (HAR) has undergone significant research in the healthcare domain. HAR utilised with powerful technologies can benefit remote patient monitoring, the elderly, patients suffering from chronic illness and ambient assisted living. Human activity recognition has shown to be effective in benefiting clinicians in the treatment and remote monitoring of patients. This field is not only vital for diagnosis and treatment, but also an assessment of how likely a medical patient will fall ill or die from certain diseases or health problems. To show the great importance of activity recognition in the health sector, analytically driving an improvement in accuracy in classifying patients' activities improves the relationship of patients and clinicians as well as reducing the possibility of a fatality. With Artificial Intelligence at the forefront of its revolutionary capabilities, a bright future is in store if we can implement it beneficially into our healthcare service. This book reveals how.

Download or read book Body Sensor Networks written by Guang-Zhong Yang and published by Springer. This book was released on 2014-04-16 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for wellbeing and healthcare. One key development in this area is wireless, wearable and implantable in vivo monitoring and intervention. A myriad of platforms are now available from both academic institutions and commercial organisations. They permit the management of patients with both acute and chronic symptoms, including diabetes, cardiovascular diseases, treatment of epilepsy and other debilitating neurological disorders. Despite extensive developments in sensing technologies, there are significant research issues related to system integration, sensor miniaturisation, low-power sensor interface, wireless telemetry and signal processing. In the 2nd edition of this popular and authoritative reference on Body Sensor Networks (BSN), major topics related to the latest technological developments and potential clinical applications are discussed, with contents covering. Biosensor Design, Interfacing and Nanotechnology Wireless Communication and Network Topologies Communication Protocols and Standards Energy Harvesting and Power Delivery Ultra-low Power Bio-inspired Processing Multi-sensor Fusion and Context Aware Sensing Autonomic Sensing Wearable, Ingestible Sensor Integration and Exemplar Applications System Integration and Wireless Sensor Microsystems The book also provides a comprehensive review of the current wireless sensor development platforms and a step-by-step guide to developing your own BSN applications through the use of the BSN development kit.

Download or read book Human Activity Recognition Challenge written by Md Atiqur Rahman Ahad and published by Springer Nature. This book was released on 2020-11-20 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).

Download or read book Human Activity Recognition and Behaviour Analysis written by Liming Chen and published by Springer. This book was released on 2019-06-11 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book IoT Sensor Based Activity Recognition written by Md Atiqur Rahman Ahad and published by Springer Nature. This book was released on 2020-07-30 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Analytics and Applications of the Wearable Sensors in Healthcare written by Shabbir Syed-Abdul and published by MDPI. This book was released on 2020-06-17 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue presents 28 research studies from 137 authors representing 37 institutions from 19 countries. To facilitate the understanding of the research articles, we have organized the book to show various aspects covered in this field, such as eHealth, technology-integrated research, prediction models, rehabilitation studies, prototype systems, community health studies, ergonomics design systems, technology acceptance model evaluation studies, telemonitoring systems, warning systems, application of sensors in sports studies, clinical systems, feasibility studies, geographical location based systems, tracking systems, observational studies, risk assessment studies, human activity recognition systems, impact measurement systems, and a systematic review. We would like to take this opportunity to invite high quality research articles for our next Special Issue entitled “Digital Health and Smart Sensors for Better Management of Cancer and Chronic Diseases” as a part of Sensors journal.

Download or read book Human Activity Recognition Using a Wearable Camera written by Girmaw Abebe Tadesse and published by . This book was released on 2020 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features.Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance.We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.

Download or read book Proceedings of the International Conference on Paradigms of Communication Computing and Data Sciences written by Mohit Dua and published by Springer Nature. This book was released on 2022-01-01 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected high-quality research papers presented at the International Conference on Paradigms of Communication, Computing and Data Sciences (PCCDS 2021), held at the National Institute of Technology, Kurukshetra, India, during May 07–09, 2021. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications, and data science techniques. The book is a collection of latest research articles in computation algorithm, communication, and data sciences, intertwined with each other for efficiency.