Self Powered Wearable Iot Devices For Health And Activity Monitoring

Wearable devices have the potential to transform multiple facets of human life, including healthcare, activity monitoring, and interaction with computers. At the same time, a number of technical and adaption challenges hinder widespread and daily usage of wearable devices. Recent research efforts have focused on identifying these challenges and solving them such that the potential of wearable devices can be realized. In this monograph, the authors guide the reader through the state-of-the-art of wearable devices, detailing the challenges that researchers and designers face in achieving wide-adoption of the technology throughout society. The authors also identify the application areas where these devices are most likely to gain acceptance. They point the way to overcoming these challenges by detailing the recent advances in providing physically flexible designs, the energy management for such designs and finally consider some of the security and privacy aspects of wearable devices such that user compliance can be improved. This monograph serves as a comprehensive resource for challenges and solutions towards self-powered wearable devices for health and activity monitoring.

Different healthcare technologies have been in use for decades. These technologies are continuously evolving and changing the way medicine will be practiced in the future. These technologies allow medical practice from anywhere, at any time, and from any device. These technologies are mainly concerned with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, processing, and use of information in health. Recent Advancements in Smart Remote Patient Monitoring, Wearable Devices, and Diagnostics Systems provides relevant theoretical and practical frameworks, as well as the latest empirical research findings in the area. It provides insights and supports executives concerned with remote patient monitoring through wearable devices and diagnostics systems. Covering topics such as cloud computing, obesity monitoring systems, and photoacoustic imaging, this premier reference source is an essential resource for hospital administrators, medical technicians, healthcare professionals, medical students and educators, librarians, researchers, and academicians.

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors.

Pervasive Cardiac and Respiratory Monitoring Devices: Model-Based Design is the first book to combine biomedical instrumentation and model-based design. As the scope is limited to cardiac and respiratory devices only, this book offers more depth of information on these devices; focusing in on signals used for home monitoring and offering additional analysis of these devices. The author offers an insight into new industry and research trends, including advances in contactless monitoring of breathing and heart rate. Each chapter presents a section on current trends. As instrumentation as a field is becoming increasingly smart, basic signal processing is also discussed. Real case-studies for each modelling approach are used, primarily covering blood pressure, ECG and radar-based devices. This title is ideal for teaching and supporting learning as it is written in an accessible style and a solutions manual for the problem sets is provided. It will be useful to 4th year undergraduate students, graduate/masters/PhD students, early career researchers and professionals working on an interdisciplinary project; as it introduces the field and provides real world applications. For engineers this book solves the problem of how to assess and calibrate a medical device to ensure the data collected is trustworthy. For students, this book allows for trying concepts and circuits via simulations and learning modeling techniques. Students will learn concepts from this book and be ready to design bioinstrumentations devices based on specifications/requirements. Focuses on model-based design using Simscape/MATLAB; learn how to design a system and how to evaluate how different choices affect the output of the system Covers pervasive monitoring: shows how to design optimal solutions for pervasive and personalized healthcare monitoring Explores uncertainty and sensitivity analysis; understand your model better

This book covers cutting edge advancements on self-powered Internet of Things, where sensing devices can be energy-positive while capturing context from the physical world. It provides new mechanisms for activity recognition without the need of conventional inertial sensors, which demand significant energy during their operation and thus quickly deplete the batteries of internet-of-things (IoT) devices. The book offers new solutions by employing energy harvesters as activity sensors as well as power sources to enable the autonomous and self-powered operation of IoT devices without the need of human intervention. It provides useful content for graduate students as well as researchers to understand the nascent technologies of human activity, fitness and health monitoring using autonomous sensors. In particular, this book is very useful for people working on pervasive computing, activity recognition, wearable IoT, fitness/healthcare and autonomous systems. This book covers a broad range of topics related to self-powered activity recognition. The main topics of this book include wearables, IoT, energy harvesting, energy harvesters as sensors, activity recognition and self-powered operation of IoT devices. This book starts with the introduction of wearable IoT devices and activity recognition and then highlights the conventional activity recognition mechanisms. After that, it describes the use of energy harvesters to power the IoT devices. Later, it explores the use of various energy harvesters as activity sensors. It also proposes the use of energy harvesters as simultaneous source of energy and context information and defines the emerging concept of energy-positive sensing compared to conventional energy-negative sensing. Finally, it explores sensor/signal fusion to enhance the performance using multiple energy harvesters and charts a way forward for future research in this area. This book covers all important and emerging topics that have significance in the design and implementation of autonomous wearable IoT devices. We believe that this book will lay the foundation for designing self-powered IoT devices which can ultimately replace the conventional wearable IoT devices which need regular recharging and replacement.

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.

This book covers cutting edge advancements on self-powered Internet of Things, where sensing devices can be energy-positive while capturing context from the physical world. It provides new mechanisms for activity recognition without the need of conventional inertial sensors, which demand significant energy during their operation and thus quickly deplete the batteries of internet-of-things (IoT) devices. The book offers new solutions by employing energy harvesters as activity sensors as well as power sources to enable the autonomous and self-powered operation of IoT devices without the need of human intervention. It provides useful content for graduate students as well as researchers to understand the nascent technologies of human activity, fitness and health monitoring using autonomous sensors. In particular, this book is very useful for people working on pervasive computing, activity recognition, wearable IoT, fitness/healthcare and autonomous systems. This book covers a broad range of topics related to self-powered activity recognition. The main topics of this book include wearables, IoT, energy harvesting, energy harvesters as sensors, activity recognition and self-powered operation of IoT devices. This book starts with the introduction of wearable IoT devices and activity recognition and then highlights the conventional activity recognition mechanisms. After that, it describes the use of energy harvesters to power the IoT devices. Later, it explores the use of various energy harvesters as activity sensors. It also proposes the use of energy harvesters as simultaneous source of energy and context information and defines the emerging concept of energy-positive sensing compared to conventional energy-negative sensing. Finally, it explores sensor/signal fusion to enhance the performance using multiple energy harvesters and charts a way forward for future research in this area. This book covers all important and emerging topics that have significance in the design and implementation of autonomous wearable IoT devices. We believe that this book will lay the foundation for designing self-powered IoT devices which can ultimately replace the conventional wearable IoT devices which need regular recharging and replacement.

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.