Smart Wearable Devices In Healthcare Methodologies Applications And Algorithms

Wearable health devices have been an emerging technology that enables an ambulatory acquisition of physiological signals to monitor health status over a long time (hours/days/weeks/years) inside and outside clinical environments. Big data and deep learning, in particular, are receiving a lot of attention in this rapidly growing digital health community. A key benefit of deep learning is to analyze and learn massive amounts of data, which makes it especially valuable in healthcare since raw data is largely gathered from personalized wearable health devices. A wide range of users may benefit from unobstructed and even remote monitoring of pertinent or vital signs, which makes it easier to detect life-threatening diseases early, track the progression of pathologies and stress levels, evaluate the efficacy of therapies, provide low-cost and reliable diagnoses, etc. Today’s personal health devices have provided an amazing insight into people’s health and wellness, which allow clinicians to use these smart wearables to collect and analyze measuring data like electroencephalogram (EEG), electrocardiogram (ECG or EKG), respiration, heart rate, temperature level, blood oxygen, and blood pressure for health monitoring or clinical trials. This Research Topic mainly focuses on the technical revolution in wearable health systems, which aims to design more smart and useful wearables, contributing to a substantial change in the methodologies, applications, and algorithms of machine learning for wearable health devices. With the help of deep learning and sensor fusion capabilities from wearable health platforms, this data will be used more effectively, which can help to construct smart, novel, specific solutions to improve the quality of healthcare and capabilities of utilizing new deep learning technologies.

Wearable Telemedicine Technology for the Healthcare Industry: Product Design and Development focuses on recent advances and benefits of wearable telemedicine techniques for remote health monitoring and prevention of chronic conditions, providing real time feedback and help with rehabilitation and biomedical applications. Readers will learn about various techniques used by software engineers, computer scientists and biomedical engineers to apply intelligent systems, artificial intelligence, machine learning, virtual reality and augmented reality to gather, transmit, analyze and deliver real-time clinical and biological data to clinicians, patients and researchers. Wearable telemedicine technology is currently establishing its place with large-scale impact in many healthcare sectors because information about patient health conditions can be gathered anytime and anywhere outside of traditional clinical settings, hence saving time, money and even lives. Provides readers with methods and applications for wearable devices for ubiquitous health and activity monitoring, wearable biosensors, wearable app development and management using machine learning techniques, and more Integrates coverage of a number of key wearable technologies, such as ubiquitous textile systems for movement disorders, remote surgery using telemedicine, intelligent computing algorithms for smart wearable healthcare devices, blockchain, and more Provides readers with in-depth coverage of wearable product design and development

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.

Internet of Healthcare Things (IoHT) is an Internet of Things (IoT)-based solution that includes a network architecture which allows the connection between a patient and healthcare facilities. This book covers various research issues of smart and secure IoHT, aimed at providing solutions for remote healthcare monitoring using pertinent techniques. Applications of machine learning techniques and data analytics in IoHT, along with the latest communication and networking technologies and cloud computing, are also discussed. Features: Provides a detailed introduction to IoHT and its applications Reviews underlying sensor and hardware technologies Includes recent advances in the IoHT, such as remote healthcare monitoring and wearable devices Explores applications of data analytics/data mining in IoHT, including data management and data governance Focuses on regulatory and compliance issues in IoHT This book is intended for graduate students and researchers in Bioinformatics, Biomedical Engineering, Big Data and Analytics, Data Mining, and Information Management, IoT and Computer and Electrical Engineering.

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniques and deploying novel technologies in intelligent healthcare services and applications. Describes the advances of computing methodologies for life and medical science data; Presents applications of artificial intelligence in healthcare along with case studies and datasets; Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

This book provides both the developers and the users with an awareness of the challenges and opportunities of advancements in healthcare paradigm with the application and availability of advanced hardware, software, tools, technique or algorithm development stemming the Internet of Things. The book helps readers to bridge the gap in their three understanding of three major domains and their interconnections: Hardware tested and software APP development for data collection, intelligent protocols for analysis and knowledge extraction. Medical expertise to interpret extracted knowledge towards disease prediction or diagnosis and support. Security experts to ensure data correctness for precise advice. The book provides state-of-the-art overviews by active researchers, technically elaborating healthcare architectures/frameworks, protocols, algorithms, methodologies followed by experimental results and evaluation. Future direction and scope will be precisely documented for interested readers.

The book explores the convergence of healthcare and cutting-edge technology, making it a captivating subject for readers interested in future research. Smart healthcare with machine learning techniques offers a transformative paradigm that utilizes the power of new technology, data analytics, and interconnected devices to enhance the quality, efficiency, and accessibility of healthcare services. This involves leveraging Internet of Things (IoT) devices, wearable technology, and machine learning algorithms to monitor patient health, predict medical conditions, and offer personalized treatment recommendations. This innovative combination not only enhances diagnostics and treatment but also addresses the research challenges of healthcare access and delivery in an increasingly connected world. By exploring the synergy between smart healthcare and machine learning, the book helps to understand how these technologies can collaborate to revolutionize patient care and healthcare delivery. This book is an outcome with applications of future technologies to overcome the toughest humanitarian challenges from an engineering approach.

This book constitutes the refereed post-conference proceedings of the Second EAI International Conference on Wearables in Healthcare, HealthWear 2020. Due to COVID-19 pandemic the conference was held virtually. The 16 revised full papers were carefully reviewed and selected from 40 submissions. They focus on wearable devices and systems for healthcare and wellbeing. The papers are organized in topical sections as follows: PPG and algorithms focusing on photoplethysmography, PPG monitoring and cardiorespiratory measurement. The next section focus on IoT and smart sensors on the use of wearable devices and systems for Internet of Medical Things application. The third section is a new session introducing wearable applications. This track focuses on the intrinsic multidisciplinary of wearable devices, and includes works on methodology and design aspect of wearable research.