U-Healthcare Monitoring Systems

Volume 1: Design and Applications

Nilanjan Dey, Amira S. Ashour, Simon James Fong, Surekha Borra

• Publisher: Academic Press
• Year: 2018
• Print ISBN:
  9780128153703
• eBook ISBN:
  9780128156384
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

U-Healthcare Monitoring Systems: Volume One: Design and Applications focuses on designing efficient U-healthcare systems which require the integration and development of information technology service/facilities, wireless sensors technology, wireless communication tools, and localization techniques, along with health management monitoring, including increased commercialized service or trial services. These u-healthcare systems allow users to check and remotely manage the health conditions of their parents. Furthermore, context-aware service in u-healthcare systems includes a computer which provides an intelligent service based on the user's different conditions by outlining appropriate information relevant to the user's situation. This volume will help engineers design sensors, wireless systems and wireless communication embedded systems to provide an integrated u-healthcare monitoring system. This volume provides readers with a solid basis in the design and applications of u-healthcare monitoring systems. - Provides a solid basis in the design and applications of the u-healthcare monitoring systems - Illustrates the concept of the u-healthcare monitoring system and its requirements, with a specific focus on the medical sensors and wireless sensors communication - Presents a multidisciplinary volume that includes different applications of the monitoring system which highlight the main features for biomedical sensor devices

Chapter 1

Wearable U-HRM device for rural applications

Mohd Imran; Mohammad Abdul Qadeer Department of Computer Engineering, Aligarh Muslim University, Aligarh, India

Abstract

This work contributes to the future technology of ubiquitous healthcare monitoring facilities where health issues regarding diagnosis are not a complex problem. The diagnosis of the disease is a prior concern in healthcare. Nowadays, heart-related diseases, which were rare in earlier eras, are commonly found in people of all ages. So it has become essential to develop a heart-monitoring device that will also sense body temperature, and do so at a promising and reasonable cost. It is a revolutionary technology that is helpful in rural areas due to its economic status and user-friendly nature. This device embraces the well-assured quality, validate, accurate, and easy to use anywhere and anytime. We have proposed a standard, integrated, and implantable device for measuring the heart rate. By employing fingertip reading, this device improves the estimation of the body's heart rate and also accounts for the body's temperature in the contextual information generated by the biosignal of the body. It has three phases: pulse detection from the index finger, then extracting the signals generated, and amplifying the pulse in order to determine the heart rate. Due to its small size and wearable nature, this device can be implanted anywhere on the body; the person just puts a fingertip on a sensor and it will calibrate the measurement accordingly. However, most of the HRM devices available in the market are costly and not effective for a daily clinical prospective. On the other hand, our HRM module is an ergonomically standardized U-healthcare integrated device that is helpful in rural areas.

Keywords

U-HRM; Internet of things (IoT); Wearable devices; Ubiquitous computing; Noncommunicative disease; Data repudiation; Data integration; End-to-end encryption

Chapter Outline

• 1 Introduction
• 2 U-Healthcare System in India
• 3 Application
• 4 Open Issues and Problems
• 5 Requirements of a Healthcare System
• 6 Requirement of Wearable Devices
• 7 Implementation
• 8 Measurement of Heart Rate and Body Temperature
• 9 Discussion
• 10 Conclusion and Future Trends
• References

1 Introduction

In the present era, IoT devices, which are a collection of multiple sensors embedded on a small single chip that is also called wearable tech, are the most common technology that continuously observes the surrounding ambient environment of a subject, then processes and analyzes that to display the desired outcome [1]. The number of wearable devices is growing at an explosive rate. According to estimates, if the rate of expansion continues, then by the year of 2020, wearable devices connected together in the world will number approximately 26 billion. This wearable technology is very reliable for measurement and early detection of diseases. These miniature sensors have a very productive application in the medicine and technology fields. The prime focus of researchers is to develop a hybridized environment where the human body is blended with these small sensors, either embedded in garments or implanted as an integrated device. They would then continuously track the biosignals generated in the body and feed the system with enormous clinical data [2]. The main objectives of integrated wearable sensors are: (1) design and development that can accurately and obtrusively detect and record biosignals in the body, (2) deliver the biosignal data gathered through continuous monitoring to the clinical experts in a form that is entirely acceptable, and (3) design of an algorithm that can take these clinical data as an input and successfully extract the relevant data to generate possible outcomes of these physiological signals [3].

This wearable technology has tremendous applications in rehabilitation and continuous monitoring because diseases are growing at an exponential rate these days. Six out of ten people are suffering from various diseases, some of which are chronic or inherited such as diabetes, color blindness, etc. According to the data given by the World Health Organization [4], lung cancer, diabetes [5], obesity, high blood pressure, and cholesterol, are the major diseases that cause suffering all around the world. According to data provided by WHO, 4.9 million people are suffering from lung cancer due to excessive consumption of alcohol, and 7.1 million people are suffering from high blood pressure [6].

The worst habit of people is to avoid the symptoms as long as possible, which can sometimes lead to death. There are various possible reasons for such mistakes, including a lack of proper equipment as well as the unavailability of professional advisors and hospitals for special care. In underdeveloped countries, where all these problems are common, the ubiquitous healthcare monitoring system is a boon. Because these pervasive monitoring systems are small in size, less expensive, and easy to use, any person can use them anywhere, anytime.

Big data analytical tools are also an important key to the ubiquitous healthcare system [7]. By using analytical tools such as Hadoop, Sparks, etc., a huge medical database can be generated demographically [8]. Features can be extracted using any method such as component-based methods in order to classify the properties of collected data [5], By using distributed computing, we can upload the basic symptoms that may be affected by demographic reasons. Therefore, mining the relevant data and the important and relevant keys to disease can be obtained, leading to more effective cures. When disease and its symptoms and possible solutions are available on a medical repository, this will be more reliable and effective for people around the globe.

In the near future, people will be surrounded by wearable ubiquitous wireless equipment that will assist them in all the needs of daily life. As the days pass by, people are indulging in a larger tech environment rather than sticking with traditional methods. In recent years, various studies have been conducted to develop a full bodysuit that is composed of numerous sensor-monitoring circuits that can provide multiple applications, such as electrocardiogram (ECG), skin temperature measurement, etc. Researchers at the MIT media lab have developed MIThrill [9], which is a wearable platform consisting of sensors and monitors to examine the human body. The same body suit is also used for gait analysis for understanding human behavior and cognitive computing. For the gait, MIThrill is equipped with rate gyros, a three-axis accelerometer, and a pressure sensor. A human body is the best workplace for exploring and understanding cognitive computing as it is a source of biosignals, which are responsible for many routine processes. This ubiquitous monitoring device focuses on the human body by continuously sensing and measuring physical phenomena. This technology has extensive applications in the medical field. By wearing these small ubiquitous healthcare devices, people can diagnose their body by themselves, thereby eliminating or reducing the need for clinical assistance. The data generated by diagnosis can also help the doctor in their treatment.

In brief, we will see that wearable technology has increased t...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1: Wearable U-HRM device for rural applications
• Chapter 2: A robust framework for optimum feature extraction and recognition of P300 from raw EEG
• Chapter 3: Medical image diagnosis for disease detection: A deep learning approach
• Chapter 4: Reasoning methodologies in clinical decision support systems: A literature review
• Chapter 5: Embedded healthcare system for day-to-day fitness, chronic kidney disease, and congestive heart failure
• Chapter 6: Comparison of multiclass and hierarchical CAC design for benign and malignant hepatic tumors
• Chapter 7: Ontology enhanced fuzzy clinical decision support system
• Chapter 8: Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule
• Chapter 9: Machine learning for medical diagnosis: A neural network classifier optimized via the directed bee colony optimization algorithm
• Chapter 10: A genetic algorithm-based metaheuristic approach to customize a computer-aided classification system for enhanced screen film mammograms
• Chapter 11: Embedded healthcare system based on bioimpedance analysis for identification and classification of skin diseases in Indian context
• Chapter 12: A hybrid CAD system design for liver diseases using clinical and radiological data
• Chapter 13: Ontology-based electronic health record semantic interoperability: A survey
• Chapter 14: A unified fuzzy ontology for distributed electronic health record semantic interoperability
• Index