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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: 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: Yahya Rahmat-Samii
Publisher: John Wiley & Sons
ISBN: 1119683297
Category : Technology & Engineering
Languages : en
Pages : 624
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Book Description
A guide to the theory and recent development in the medical use of antenna technology Antenna and Sensor Technologies in Modern Medical Applications offers a comprehensive review of the theoretical background, design, and the latest developments in the application of antenna technology. Written by two experts in the field, the book presents the most recent research in the burgeoning field of wireless medical telemetry and sensing that covers both wearable and implantable antenna and sensor technologies. The authors review the integrated devices that include various types of sensors wired within a wearable garment that can be paired with external devices. The text covers important developments in sensor-integrated clothing that are synonymous with athletic apparel with built-in electronics. Information on implantable devices is also covered. The book explores technologies that utilize both inductive coupling and far field propagation. These include minimally invasive microwave ablation antennas, wireless targeted drug delivery, and much more. This important book: Covers recent developments in wireless medical telemetry Reviews the theory and design of in vitro/in vivo testing Explores emerging technologies in 2D and 3D printing of antenna/sensor fabrication Includes a chapter with an annotated list of the most comprehensive and important references in the field Written for students of engineering and antenna and sensor engineers, Antenna and Sensor Technologies in Modern Medical Applications is an essential guide to understanding human body interaction with antennas and sensors.
Author: Edward Sazonov
Publisher: Elsevier
ISBN: 0124186661
Category : Technology & Engineering
Languages : en
Pages : 649
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Book Description
Written by industry experts, this book aims to provide you with an understanding of how to design and work with wearable sensors. Together these insights provide the first single source of information on wearable sensors that would be a valuable addition to the library of any engineer interested in this field. Wearable Sensors covers a wide variety of topics associated with the development and application of various wearable sensors. It also provides an overview and coherent summary of many aspects of current wearable sensor technology. Both industry professionals and academic researchers will benefit from this comprehensive reference which contains the most up-to-date information on the advancement of lightweight hardware, energy harvesting, signal processing, and wireless communications and networks. Practical problems with smart fabrics, biomonitoring and health informatics are all addressed, plus end user centric design, ethical and safety issues. Provides the first comprehensive resource of all currently used wearable devices in an accessible and structured manner Helps engineers manufacture wearable devices with information on current technologies, with a focus on end user needs and recycling requirements Combines the expertise of professionals and academics in one practical and applied source
Author: Swarnendu Kar
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Book Description
A wearable sensor system is disclosed that provides a measurable magnetic field that changes horizontally within the range of motion of human limbs. The wearable sensor system includes a magnetic sensing device, and one or more magnet devices that provide the measurable magnetic field with a strength exceeding the Earth's magnetic field. To this end, the magnetic sensing system provides a "personal" magnetic field about a user, with that magnetic field traveling with the user and overpowering adjacent interfering fields. The wearable sensor system may include a sensor arrangement that measures a strength of the personal magnetic field and field direction to perform horizontal localization, and may send a representation of a same to a remote computing device to cause an action to occur. Some such actions include output of pre-recorded or synthesized musical notes, for example.
Author: Daniel Yi Xu
Publisher:
ISBN:
Category : Dielectric devices
Languages : en
Pages : 200
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Book Description
From improving our technique in sports to providing feedback for rehabilitation therapy, wearable motion capture sensors have the potential to greatly enhance and improve our lives. While traditional camera-based systems have limited usage outdoors, wearable sensors have the ability to follow us from our workplace to our homes, and continuously provide feedback on how we’re moving, anywhere and at any time. One promising candidate for a wearable sensor is the dielectric elastomer (DE), a soft, flexible and highly stretchable polymer. In order to use DE sensors for motion capture, we need to be able to measure their capacitance, both accurately and efficiently. However, the large majority of low cost DEs have non-ideal electrode properties that cause problems for traditional capacitance sensing methods. Existing DE sensing methods were mainly developed for high voltage applications and lack the efficiency, safety and scalability to be implemented on a large scale, such as in a sensing suit. This thesis addresses these sensing challenges. First, we present a new low voltage hardware design that can measure the capacitance of multiple DEs at the same time. Then, in order to reduce computational power, we discuss the design of an efficient capacitance circuit that can increase the monitoring period of the sensor by an order of magnitude. We also quantify, for the first time, how the sensing frequency can affect the accuracy of the capacitance measurement. This new model presents a new design guide on how to select a suitable frequency for any sensor design. Afterwards, we demonstrate the ability to sense local capacitance changes within the sensor. This breakthrough significantly helps to reduce the amount of wires, connectors and sensing electronics for large sensor systems, a key step for increasing the scalability of these systems. Finally, we extend our method for strain mapping into two dimensions, producing a soft touch keyboard. The new low voltage capacitance sensing methods developed in this thesis are efficient, accurate and highly scalable. These improvements are key enablers that will allow DEs to be used as wearable motion capture sensors.
Author: Dirk Weenk
Publisher:
ISBN: 9789036539722
Category :
Languages : en
Pages : 121
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Book Description
Author: Shiban Kishen Koul
Publisher: Springer Nature
ISBN: 9811639736
Category : Technology & Engineering
Languages : en
Pages : 336
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Book Description
This book presents state-of-the-art technologies, trends and applications with a focus on the healthcare domain for ultra-wideband (3.1–10.6 GHz) and 60 GHz (57–66 GHz) wireless communication systems. Due to various key features such as miniaturized antenna design, low power, high data rate, less effects on the human body, relatively less crowded spectrum, these technologies are becoming popular in various fields of biomedical applications and day-to-day life. The book highlights various aspects of these technologies related to body-centric communication, including antenna design requirements, channel modeling and characterization for WBANs, current fabrication and antenna design strategies for textile, flexible and implanted antennas. Apart from the general requirements and study related to these frequency bands, various application specific topics such as localization and tracking, physical activity recognition and assessment, vital sign monitoring and medical imaging are covered in detail. The book concludes with the glimpses of future aspects of the UWB and 60 GHz technology which includes IoT for healthcare and smart living, novel antenna materials and application of machine learning algorithms for overall performance enhancement.
Author: Chen, Xi (Researcher on human mechanics)
Publisher:
ISBN:
Category : Human locomotion
Languages : en
Pages : 169
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Book Description
High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary. In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson's disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user's itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system.
Author: Robert Charles LeMoyne
Publisher: Springer Nature
ISBN: 9819724392
Category : Electronic books
Languages : en
Pages : 206
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Book Description
This book is the second edition of the one originally published in 2017. The original publication features the discovery of numerous novel applications for the use of smartphones and portable media devices for the quantification of gait, reflex response, and an assortment of other concepts that constitute first-in-the-world applications for these devices. Since the first edition, numerous evolutions involving the domain of wearable and wireless systems for healthcare have transpired warranting the publication of the second edition. This volume covers wearable and wireless systems for healthcare that are far more oriented to the unique requirements of the biomedical domain. The paradigm-shifting new wearables have been successfully applied to gait analysis, homebound therapy, and quantifiable exercise. Additionally, the confluence of wearable and wireless systems for healthcare with deep learning and neuromorphic applications for classification is addressed. The authors expect that these significant developments make this book valuable for all readers.
Author: Delaram Yazdansepas
Publisher:
ISBN:
Category :
Languages : en
Pages : 296
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Book Description
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.
