Wearable Sensors and Machine Learning Based Human Movement Analysis PDF Download
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Author: Bernd Stetter
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Bernd Stetter
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Matteo Zago
Publisher: Frontiers Media SA
ISBN: 2889665615
Category : Science
Languages : en
Pages : 328
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Book Description
Author: Shabbir Syed-Abdul
Publisher: MDPI
ISBN: 3039363506
Category : Medical
Languages : en
Pages : 498
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Book Description
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.
Author: Yuan Xi
Publisher: OAE Publishing Inc.
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 34
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Book Description
Self-powered wearable Internet of Things (IoT) sensors have made a significant impact on human life and health in recent years. These sensors are known for their convenience, durability, affordability, and longevity, leading to substantial improvements in people’s lives. This review summarizes the development of self-powered wearable IoT sensors in recent years. Materials for self-powered wearable sensors are summarized and evaluated, including nanomaterials, flexible materials, and degradable materials. The working mode of self-powered wearable IoT sensors is analyzed, and the different principles of its physical sensing and chemical sensing are explained. Several common technologies for self-powered wearable IoT sensors are presented, such as triboelectric technology, piezoelectric technology, and machine learning. The applications of self-powered IoT wearable sensors in human-machine interfaces are reviewed. Its current shortcomings and prospects for its future development are also discussed. To conduct this review, a comprehensive literature search was performed using several electronic databases, resulting in the inclusion of 225 articles. The gathered data was extracted, synthesized, and analyzed using a thematic analysis approach. This review provides a comprehensive analysis and summary of its working mode, technologies, and applications and provides references and inspiration for related research in this field. Furthermore, this review also identifies the key directions and challenges for future research.
Author: Robert LeMoyne
Publisher: Springer
ISBN: 9811056846
Category : Technology & Engineering
Languages : en
Pages : 142
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Book Description
This book provides visionary perspective and interpretation regarding the role of wearable and wireless systems for the domain of gait and reflex response quantification. These observations are brought together in their application to smartphones and other portable media devices to quantify gait and reflex response in the context of machine learning for diagnostic classification and integration with the Internet of things and cloud computing. The perspective of this book is from the first-in-the-world application of these devices, as in smartphones, for quantifying gait and reflex response, to the current state of the art. Dr. LeMoyne has published multiple groundbreaking applications using smartphones and portable media devices to quantify gait and reflex response.
Author: Shaghayegh Zihajehzadeh
Publisher:
ISBN:
Category :
Languages : en
Pages : 115
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Book Description
Recent advances in MEMS wearable inertial/magnetic sensors and mobile computing have fostered a dramatic growth of interest for ambulatory human motion capture (MoCap). Compared to traditional optical MoCap systems such as the optical systems, inertial (i.e. accelerometer and gyroscope) and magnetic sensors do not require external fixtures such as cameras. Hence, they do not have in-the-lab measurement limitations and thus are ideal for ambulatory applications. However, due to the manufacturing process of MEMS sensors, existing wearable MoCap systems suffer from drift error and accuracy degradation over time caused by time-varying bias. The goal of this research is to develop algorithms based on multi-sensor fusion and machine learning techniques for precise tracking of human motion and location using wearable inertial sensors integrated with absolute localization technologies. The main focus of this research is on true ambulatory applications in active sports (e.g., skiing) and entertainment (e.g., gaming and filmmaking), and health-status monitoring. For active sports and entertainment applications, a novel sensor fusion algorithm is developed to fuse inertial data with magnetic field information and provide drift-free estimation of human body segment orientation. This concept is further extended to provide ubiquitous indoor/outdoor localization by fusing wearable inertial/magnetic sensors with global navigation satellite system (GNSS), barometric pressure sensor and ultra-wideband (UWB) localization technology. For health applications, this research is focused on longitudinal tracking of walking speed as a fundamental indicator of human well-being. A regression model is developed to map inertial information from a single waist or ankle-worn sensor to walking speed. This approach is further developed to estimate walking speed using a wrist-worn device (e.g., a smartwatch) by extracting the arm swing motion intensity and frequency by combining sensor fusion and principal component analysis.
Author: Md Atiqur Rahman Ahad
Publisher: Springer Nature
ISBN: 9811903611
Category : Technology & Engineering
Languages : en
Pages : 268
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Book Description
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.
Author: Jorge Luis Reyes Ortiz
Publisher: Springer
ISBN: 3319142747
Category : Technology & Engineering
Languages : en
Pages : 147
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Book Description
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.
Author: Miguel A. Labrador
Publisher: CRC Press
ISBN: 1466588276
Category : Computers
Languages : en
Pages : 209
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Book Description
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.
Author: Samaneh Davarzani
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Gait recognition systems have gained tremendous attention due to its potential applications in healthcare, criminal investigation, sports biomechanics, and so forth. A new solution to gait recognition tasks can be provided by wearable sensors integrated in wearable objects or mobile devices. In this research a sock prototype designed with embedded soft robotic sensors (SRS) is implemented to measure foot ankle kinematic and kinetic data during three experiments designed to track participants’ feet ankle movement. Deep learning and statistical methods have been employed to model SRS data against Motion capture system (MoCap) to determine their ability to provide accurate kinematic and kinetic data using SRS measurements. In the first study, the capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of twenty participants on a flat surface and a cross-sloped surface. I have conducted another study regarding kinematic features in which deep learning models were trained to estimate the joint angles in sagittal and frontal planes measured by a MoCap system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. The focus of the last study is measuring the kinetic features and the goal is determining the validity of SRS measurements, to this end the pressure data measured with SRS embedded into the sock prototype would be compared with the force plate data.
