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Rami Ghannam,
University of Glasgow
Glasgow
UK
Muhammad A. Imran
Qammer H. Abbasi
You Hou
Yuchi Liu
Yidi Xiao
Guodong Zhao
Francesco Fioranelli
Technical University of Delft
Netherlands
Julien Le Kernec
Ahmed Zoha
Janet Bouttell
Eleanor Grieve
Neil Hawkins
Naem Ramzan
University of West of Scotland
Along with Medicine and Law, Engineering is one of the oldest professions in the world. While there is debate regarding the exact definition of engineering, the word engineer derives itself from the Latin word ingenium, which means ingenuity Wall [2010]. In fact, engineering involves the application of science and technology to develop new products, tools, services or processes that can benefit society Crawley et al. [2007]. According to Theodore Von Krmn: "Scientists discover the world that exists; engineers create the world that never was" Mackay [1991]. Consequently, engineers are problem solvers and are responsible for creating the healthcare products that we see today.
During the past two hundred and fifty years, the field of engineering has witnessed several waves of innovation, which have depended on the world's techno-economic paradigm shifts Perez [2010]. Each of the these overlapping waves are approximately 50 years in duration and are also known as the Kwaves. Thanks to the use of iron, waterpower and mechanical constructions, the first of these innovation waves started with the industrial revolution in 18th Century Britain de Graaff and Kolmos [2014]. Now, as we approach the 21st century and the sixth innovation wave, engineers are shifting their interests from the fields of physics, electronics and communications to the interdisciplinary fields of biology and information technology.
Thus, during the past two decades the healthcare industry has seen a rapid transformation. In fact, medical technologies have evolved since the development of the bifocal lens in the early 18th century by Benjamin Franklin. Today, engineers are transforming these contact lenses into healthcare platforms that can monitor vital human signs Yuan et al. [2020]. Consequently, the healthcare field is continuously being reshaped through advances in sensors, robotics, microelectronics, big data and artificial intelligence.
Innovation in healthcare is now a widely researched topic. It is currently a "hot" topic, since it is desperately needed. Without doubt, innovation allows us to think differently, to take risks and to develop ideas that are far better than existing solutions. In this book, we aim to highlight the research that engineers have been engaged in for developing the next generation of healthcare technologies.
Currently, there is no book that covers all topics related to microelectronics, sensors, data, system integration and healthcare technology assessment in one reference. This book aims to critically evaluate current state-of-the-art technologies and will provide readers with insights into developing new solutions.
The book discusses how advances in sensing technology, computer science, communications systems and proteomics/genomics are influencing healthcare technology.
Our book is highly beneficial for healthcare executives, managers, technologists, data scientists, clinicians, engineers and industry professionals to help them identify realistic and cost-effective concepts uniquely tailored to support specific healthcare challenges. Moreover, researchers, professors, doctorate and postgraduate students would also benefit from this book, as it would enable them to identify open issues and classify their research based on existing literature. In fact, these academics need to ensure that their curricula are constantly being revised and updated according to the previously mentioned innovation waves Ahmad et al., Magjarevic et al. [2010], Xeni et al..
Additionally, our book aims to provide in-depth knowledge to stakeholders, regulators, institutional actors, research agencies on the latest developments in this field, which serves as an aid to making the right choices in prioritizing funding resources for the next generation of healthcare technologies. The first chapter deals with Healthcare Technology Assessment (HTA). This chapter focuses on three main topics. The first aims to provide an explanation of the principles of HTA and its familiar role in determining coverage of health care provision. The second involves outlining the challenges of health technology assessment for medical devices. An outline of the main categories of devices will be presented (large capital items, point of care devices, diagnostics, implantables and telehealth) and the difficulties associated with evaluating each of these types of devices. Challenges include licensing and regulation, incremental improvement, evidence generation, short lifespan, workflow, behavioural and other contextual factors and indirect health benefit. Finally, the authors will mention the contribution of HTA in the development and translation of medical devices. They will set out the role of HTA in identifying needs, assessing the potential of technologies in development, aiding design and tailoring evidence generation activities. The chapter will also be Illustrated with appropriate case studies.
Chapter 2 deals with contactless RF sensing, which has recently gained plenty of interest in the domain of healthcare and assisted living due to its capability to monitor several parameters related to the health and well-being of people. This ranges from respiration and heartbeat to gait and mobility, to activity patterns and behaviour. The main advantage of RF sensing is its contactless monitoring capability. Consequently, no sensors need to be worn by the person monitored and no optical images need to be taken via conventional cameras, which can raise problems of privacy especially in private homes. The aim of this chapter is to provide an overview of the most recent different RF technologies for healthcare, including active and passive radar and wireless channel information.
Chapter 3 discusses recent advances in Pervasive Sensing. Here, the vision of nanoscale networking attempts to achieve the functionality and performance of the Internet with the exceptions that node size is measured in cubic nanometres and channels are physically separated by up to hundreds or thousands of nanometres. In addition, these nano-nodes are assumed to be self-powered, mobile and rapidly deployable in and around a specific target. Nevertheless, downscaling the principles of traditional electromagnetic networks to the nanoscale introduces several challenges, both in terms of device technologies and communication solutions. This chapter will shed light on the basic principles of nano-electromagnetic communication in the Terahertz frequency region in the nanoscale dimension.
Moreover, chapter 4 is concerned with providing recent advances in Microelectronics for Brain Implants. This Chapter discusses advances in diagnosis, monitoring, management and treatment of neurological disorders. It will be two parts: first we will discuss our approaches for in vitro diagnostics include lab-on-chip progresses for neurodegenerative diseases such as Alzheimers and Parkinsons diseases. Secondly, we will review our in-vivo implantable medical devices for different applications include treatments of epilepsy and spinal cord. We will conclude this chapter from different perspective including sensing, communications and energy harvesting.
Chapter 5 describes the rationale for using machine learning (ML) techniques for decision making in the healthcare industry. In human physiology, hydration is essential for the proper functioning of multiple systems. Hydration is responsible for controlling various biological reactions by acting as a solvent, a reaction medium, a reactant and a reaction product. Water is the major component of the human body, making it critical for thermoregulation, cell volumes and even for joint lubrication. This chapter will deal with applying machine learning techniques on data collected from a controlled environment for detection of skin hydration levels.
In chapter 6, the authors describe how machine learning techniques can revolutionize medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability and ML has the potential to predict SNPs, which can enable the diagnosis and prognosis of several...
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