Energy Efficiency of Medical Devices and Healthcare Applications

Amr Mohamed

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128190456
• eBook ISBN: 9780128190463
• Topic: Biological Sciences
• Subtopic: Industrial Management
• Index: Biological Sciences

Energy Efficiency of Medical Devices and Healthcare Facilities provides comprehensive coverage of cutting-edge, interdisciplinary research, and commercial solutions in this field. The authors discuss energy-related challenges, such as energy-efficient design, including renewable energy, of different medical devices from a hardware and mechanical perspectives, as well as energy management solutions and techniques in healthcare networks and facilities. They also discuss energy-related trade-offs to maximize the medical devices availability, especially battery-operated ones, while providing immediate response and low latency communication in emergency situations, sustainability and robustness for chronic disease treatment, in addition to high protection against cyber-attacks that may threaten patients' lives. Finally, the book examines technologies and future trends of next generation healthcare from an energy efficiency and management point of view, such as personalized or smart health and the Internet of Medical Things — IoMT, where patients can participate in their own treatment through innovative medical devices and software applications and tools. The books applied approach makes it a useful resource for engineering researchers and practitioners of all levels involved in medical devices development, healthcare systems, and energy management of healthcare facilities. Graduate students in mechanical and electric engineering, and computer science students and professionals also benefit.- Provides in-depth knowledge and understanding of the benefits of energy efficiency in the design of medical devices and healthcare networks and facilities- Presents best practices and state-of-art techniques and commercial solutions in energy management of healthcare networks and systems- Explores key energy tradeoffs to provide scalable, robust, and effective healthcare systems and networks

Chapter 1

AI-based techniques on edge devices to optimize energy efficiency in m-Health applications

Abeer Al-Marridi, Ms ¹ , Amr Mohamed, PhD ² , and Aiman Erbad, PhD ^{1 1} Department of Computer Science and Engineering, Qatar University, Doha, Qatar ² Professor, Department of Computer Science and Engineering, Qatar University, Doha, Qatar

Abstract

The fast increase in the number of patients who need continuous monitoring by caregivers and the inequality between the number of patients compared with the number of doctors cause a burden for both doctors and patients. This one-to-one relationship poses a real scalability challenge in the healthcare systems. Resolving the problem by exploiting the fast developments in the fields of sensors, mobile phones, and wireless technologies to improve health systems is a critical approach. M-Health system accommodates the use of an edge device to send medical data over the wireless network toward the m-Health center to diagnose and control the case of the patient as fast as possible. However, the delivery of the substantial medical data is constrained by two factors, the wireless bandwidth provisioned from the network, as well as the energy consumption since edge devices limited to energy sources. As a result, implementing artificial intelligence (0) techniques at the edge devices before transmitting will enhance the overall energy efficiency of the m-Health system. Deep learning can be used on medical data to facilitate data exchange and summarization. This chapter will introduce mobile and smart health, edge computing, and different smart preprocessing techniques using AI and specifically deep neural networks to facilitate the transmission of the huge medical data from the edge devices while ensuring the optimization of energy efficiency.

Keywords

Artificial intelligence; Computing; Deep learning; Edge; Optimization; s-Health

1. Introduction

Healthcare is one of the highest priorities worldwide, where spending increases rapidly in this sector. In past years, the number of diseases increases rapidly, causing a vital rise in the number of patients compared with the number of doctors all over the world. The traditional way of communication between the patient and doctor cannot align with the situation. Owing to that, researchers consider the extensive use of mobile phones all over the world with the rapid development in technology domains, including smartphones, communication barriers, sensors, and much more, to support the shortage in health facilities.

The World Health Organization, defined that anything supports all the fields of healthcare through information and communication technology, goes under the electronic Health (e-Health) [1]. Mobile-Health (m-Health) is a subset of e-Health, which supports health objectives by deploying mobile telephone and wireless technologies [2,3].

The development of smart-phones devices raises new opportunities for researchers to integrate them into the treatment process. Therefore, smart-Health (s-Health) was defined as a component of m-Health. Smart devices eliminate the need for integrating separate sensors with the patients, as almost all these devices contain a built-in sensor for biosensing tracking [4]. Additionally, the connectivity problem will be eliminated using smart devices as the coverage of mobile cellular networks grows rapidly [2]. Fig. 1.1 is the Vann diagram that shows the relation between s-Health, m-Health, and e-Health.

Figure 1.1 A Venn diagram shows the overlapping relationships, where s...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Reviewers
• Contributors
• Preface
• Chapter 1. AI-based techniques on edge devices to optimize energy efficiency in m-Health applications
• Chapter 2. Applying an efficient evolutionary algorithm for EEG signal feature selection and classification in decision-based systems
• Chapter 3. Edge computing for energy-efficient smart health systems: Data and application-specific approaches
• Chapter 4. Energy-efficient EEG monitoring systems for wireless epileptic seizure detection
• Chapter 5. Intelligent energy-aware decision-making at the edge in healthcare using fog infrastructure
• Chapter 6. Deep learning-based security schemes for implantable medical devices
• Chapter 7. Secure medical treatment with deep learning on embedded board
• Chapter 8. Blockchain applications for healthcare
• Index