Wearable Telemedicine Technology for the Healthcare Industry: Product Design and Development

By Deepak Gupta

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

• Language: English
• Publisher: Academic Press
• Release date: Nov 16, 2021
• ISBN: 9780323858106

Book preview

Preface

This book begins with the basics of wearable telemedicine techniques and introduces the tools, methods, and advancements associated with them. It unites healthcare with a leading technology—that is, wearable telemedicine for healthcare—using the advantages of the latter to solve the problems faced by the former. The book 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 aimed at narrowing the increasing gap in provision of healthcare. This book describes the different techniques of intelligent systems from a practical point of view, aiming to solve common life problems. It presents various ranges of wearable devices for different human body parts to expedite early detection and personalized diagnosis of disease to cure patients at early stages and enable prompt responses in emergency situations. This helps wearable telemedicine techniques answer previously unanswered questions, which is their uniquely critical application in the healthcare industry. The use of telemedicine-assisted wearable devices is currently the requirement of almost all technologies/applications; there is a separate and special need to address its association with healthcare. The main objectives of telemedicine techniques in this sector are to come up with ways to provide remote-based personalized healthcare treatments to patients by considering their demographics. The book further illustrates the possible challenges in its applications and suggests ways to overcome them. The topic is wide in nature; hence, every technique and/or solution cannot be discussed in detail. The primary emphasis of this book is to introduce telemedicine techniques for disorders, challenges, and concepts to data scientists, students, and academicians at large.

Objective of the book

The main aim of this book is to provide a detailed understanding of wearable telemedicine technologies and focus on their applications in the field of healthcare. The ultimate goal is to bridge data mining and medical informatics communities to foster interdisciplinary works that can be put to good use.

Organization of the book

The book is organized into 11 chapters as follows:

1. Human Body Interaction-Driven Wearable Technology for Vital Signal Sensing

Vital signs traffic devices increase quality of life through disease control. This chapter discusses ultra-wide-band (UWB) technology-based packet transfer and hybrid scheduling–based collision monitoring and updating mechanisms between user and patient.

2. HealthWare Telemedicine Technology (HWTT) Evolution Map for Healthcare

In this chapter, analysis of wearable devices in healthcare applications is presented, along with their challenges. The chapter concludes with a discussion of potential opportunities for telemedicine analytics in the healthcare sector.

3. Blockchain: A Novel Paradigm for Secured Data Conduct in Telemedicine

This chapter presents various models and frameworks proposed in the state-of-the-art related to blockchain and discusses their implications for patient engagement and empowerment. These models are discussed in terms of their performance and cost in providing secured and private data sharing. Blockchain is now one of the most active fields of software science. By restoring authority over medical records and health data to the patient, it will shift the hierarchy of healthcare.

4. Wearable Technology and Artificial Intelligence in Psychiatric Disorders

This chapter discusses the wearable technologies and artificial intelligence (AI) related to psychiatric disorders. The collected data till now support use of AI-implemented models, other predictions, and treatments for mental disorders.

5. Applying Wearable Smart Sensors for Vital Sign Controlling of Patients in Epidemics

This chapter highlights the use of different types of sensors to improve epidemic disease control. A complete breakdown of wearable systems used in emergencies such as epidemics is presented in this chapter. Notably, implementing suitable technological solutions could enhance the management and control of epidemics and could provide constant monitoring of vital indications.

6. A Novel Compressive Sensing with Deep Learning-Based Disease Diagnosis Model for Smart Wearable Healthcare Devices

In this chapter, the authors design novel compressive sensing with a deep learning (DL)–based disease diagnosis model for smart wearable devices: the CSDDS-SW model. The presented model uses an adaptive lossless data compression technique to compress the healthcare data gathered by the wearables.

7. Blockchain-Based Secure Data Sharing Scheme Using Image Steganography and Encryption Techniques for Telemedicine Applications

The presented work designs a new Blockchain-based Secure Data Sharing Scheme (BBSDSS) using image steganography and encryption techniques for telemedicine applications. The BBSDSS model involves three stage processes: image steganography, encryption, and secure data sharing. The model includes a sign encryption technique for encrypting the stegano image. Finally, the blockchain technique is applied to enable secure sharing of patient details.

8. Intelligent Metaheuristic Cluster-Based Wearable Devices for Healthcare Monitoring in Telemedicine Systems

Telemedicine over Internet of Things (IoT) and wearable devices produces an exponential growth in data quantity that enhances transmission, processing, and archiving. The authors develop a new multi-objective water wave optimization (MOWWO) algorithm with a support vector machine (SVM), called the MOWWO-SVM model, for cluster-based healthcare monitoring using wearable devices for telemedicine systems.

9. Class Imbalance Data Handling with Deep Learning-Based Ubiquitous Healthcare Monitoring System using Wearable Devices

In this chapter, the authors developed a new class imbalance data handling with optimal deep belief network (ODBN) model, named CIH-ODBN, for a ubiquitous healthcare monitoring system. The outcome for the experimental validation verified the effective classification outcome of the CIH-ODBN model with an accuracy of 0.916 and 0.932 on the test diabetes and heart disease datasets, respectively.

10. IoT and Wearables for Detection of COVID-19 Diagnosis Using Fusion-Based Feature Extraction with Multi-Kernel Extreme Learning Machine

In this chapter, the authors presented an efficient Fusion-based Feature Extraction with Multi-Kernel Extreme Learning Machine (FFE-MKELM) for COVID-19 diagnosis using the IoT and wearables. Experimental validation of the FFE-MKELM model is performed against a benchmark dataset and the outcomes are inspected under different measures. The resultant simulation outcome ensured the betterment of the FFE-MKELM method by demonstrating an increased sensitivity of 97.34%, specificity of 97.26%, accuracy of 97.14%, and F-measure of 97.01%.

11. Internet of Things and Wearables-Enabled Alzheimer Detection and Classification Model Using Stacked Sparse Autoencoder

The proposed work introduces an effective IoT and wearables-enabled AD detection and classification model using a stacked sparse autoencoder (ADC-SSAE). The proposed ADC-SSAE model enables the wearables to collect patient data and medical examination to take place on the captured data. The experimental values obtained by the ADC-SSAE model have ensured the efficacy of the ADC-SSAE model over the compared methods.

Chapter 1

Human body interaction driven wearable technology for vital signal sensing

Thangavel Prem Jacoba, Albert Pravina, Manikandan Ramachandranb, Ambeshwar Kumarb, Deepak Guptac

aDepartment of Computer Science and Engineering, Sathyabama University, Chennai, Tamil Nadu, India bSchool of computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India cMaharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi,