Sensors for Health Monitoring

Cover
Sensors for Health
Monitoring
Copyright
Contributors
About the Editors
Preface
Part 1: U-Healthcare monitoring system using sensor networks
1
Advanced processing techniques and secure architecture for sensor networks in ubiquitous healthcare systems
	Introduction
	Challenges in ubiquitous healthcare systems
		Research challenges
		Ethical challenges
	Enabling technologies
		Wireless sensor networks
			Unobtrusiveness
			Security
			Interoperability
			Reliability
		Body area networks
	Application scenarios
		Vital sign monitoring in hospitals and care facilities
		Monitoring systems for the elderly
		Smart systems to assist the disabled
		Expanding research prospective
	Data processing techniques
	Secure architectures
	Discussion
	Conclusions
	References
	Further reading
2
Wireless sensor networks towards convenient infrastructure in the healthcare industry: A systematic study
	Introduction
	Background
	Wireless sensor network: Technical architecture
		Categories of wireless sensor networks
		How the process flows
			Battery
			Sensors
			Memory
			Typical node
			Network
		How it works
	Wireless sensor network applications in healthcare industries
		Monitoring of patients and detection of diseases
	Challenges in wireless sensor networks and wearable sensors
	Discussion and conclusion
	References
3
A comprehensive dialogue for U-body sensor network (UBSN) with experimental case study
	Introduction
	Related work
	Motivation and background
	System design
		Sensor
		Sensing, communication, and computing
		Gateway design
			Gateway limitations
			SMART gateway implementation
	Proposed methodology
		Sensor parameter
		Network setup
			Communication protocol
			Data polling
			Token ring protocol management
	Working model
		System architecture
		Data classification
		Experimental case study
	Recommendation system
	Remark
	References
4
Compressive sensing in medical signal processing and imaging systems
	Introduction
		Compressive sensing in health monitoring
	A brief review of compressive sensing and its role in health monitoring
		Compressive sensing signal acquisition
		Compressive sensed signal reconstruction
		Compressive sensing metrics
		Compressive sensing and health monitoring
	Compressive sensing of electrocardiogram
		Historical background of electrocardiogram
		Compressive sensing applications to ECG
			Reconstruction algorithms
			Body area network applications
			Compressive sensing vs. discrete wavelet transform
			Compressive sensing using the ECG structure
	Compressive sensing in magnetic resonance imaging
		Compressive sensing applications to MRI
		MRI compressive sensing requirements
	Compressive sensing in angiograms
	Compressive sensing in neuroimaging
	Compressive sensing in cardiac magnetic resonance
	Compressive sensed MRSI
	Compressive sensing in musculoskeletal system
	Compressive sensing of computed tomography
		Compressed sensing implementation on computed tomography
	Conclusion
	References
Part 2: Internet of things for U-healthcare
5
Nanopore sequencing technology and Internet of living things: A big hope for U-healthcare
	Introduction
	Nanopore sequencing technology
		Evolution of sequencing technologies
		Nanopore sequencing technologies
			MinION (2015)
			PromethION
			SmidgION
			GridION
	Role of nanopore sequencing in U-healthcare
		Applications of nanopore technologies in U-healthcare
			Virus control and surveillance
			Real-time monitoring of body fluids
			Molecular level of understanding disease mechanism
			Wastewater management
			Inferring evolutionary relationship
		Role of ONT in revolutionizing U-healthcare
		Applications of nanopore technologies in space stations
		Maintenance and issues of ONT in U-healthcare
	Internet of living things: Concepts and applications in U-healthcare
	Convergence of ONT with IoLT and other technologies for U-healthcare
	Promises, opportunities, and challenges
		ONT is expected to revolutionize healthcare
		Need for huge computing infrastructure
		IoLT and its convergences with ONT for U-healthcare
	Discussion
	Conclusion
	References
6
Internet of things-enabled virtual environment for U-health monitoring
	Understanding IoT protocols
	List of open-source and licensed IoT cloud platforms
	Introduction to MQTT
	MQTT properties
		Separation
		Scalability
		Message filtering
		Security
	IoT framework for U-health monitoring
		MQTT-based model of U-healthcare system
		Body area network
		Wearable sensors for health monitoring
		Placement of wearable sensors
		Controllers for IoT-based health monitoring
			Raspberry Pi
			Setting up Raspberry Pi
		Connecting RPi with MQTT
	References
7
Health status from your body to the cloud: The behavioral relationship between IoT and classification techniqu ...
	Introduction
	Materials and methods
		Dataset collection
		Classifier models
			J48 classifier
			K-nearest neighbor
		Performance measures
		Experimental setup
	Results and discussion
	Comparison
	Conclusion
	References
	Further reading
8
Intelligent energy-efficient healthcare models integrated with IoT and LoRa network
	Introduction: The era of smart healthcare
	Embedded intelligence and its applications
	Case studies
		Smart asthma-monitoring device
			Problem statement
			Proposed design
		Detecting symptoms of Parkinson's disease using wearable tech
			Problem statement
			Proposed design
		Smart band-aid to detect drug dosage
			Problem statement
			Proposed design
	Creating energy-efficient models
	Scope for the future
	Conclusion
	References
9
Wearable fitness band-based U-health monitoring
	Introduction
	Interfacing
	Features
	Placement of bands
	Design of fitness bands
	Energy requirement for fitness bands
		Input voltage
		Maximum current (continuous)
		Pulse current capability
		Effective capacity and lifetime
	Standardization/regulation
		Fitness apps
		Material standardization
		Wireless standardization
	Discussion
	Conclusion
	References
10
Role of trust in the ubiquitous healthcare system: Challenges and opportunities
	Introduction
		Growing expectations for better health performance
		From the packages of the past to the reforms of the future
	What is an ubiquitous healthcare
		Conventional, eHealth, and mHealth systems
		The ubiquitous healthcare
		The ubiquitous smart healthcare framework
		Gaps in the literature on ubiquitous healthcare
	Role of IoT/WSN role in ubiquitous healthcare
		IoT healthcare system design and planning
		The Internet of things addressing health, social care, and wellbeing challenges
	IoT healthcare features and opportunities for people with disabilities
		Types of disabilities
		Responses from the changing world
			IBM Watson
			Open mHealth
			Health decision support system
			Stress detection and alleviation system
			Energy-efficient health monitoring system
	Challenges of a ubiquitous healthcare system
		Accidental catastrophes
		Protecting patients confidentiality
		Deliberate disrupt
		Prevalent disrupt
		Threats of cyberattacks
		Information eavesdropping and confidentiality
		Location privacy
		Privacy of IoT-based applications
		Interoperability
		Lack of trust between service providers
		Scalability concerns
		Identity threats and privacy of stored data
		Privacy requirements for IoT applications for users with disabilities [40]
	Future concerning directions
		Cybernetic precaution
		Insufficient information arrangements and machine learning prototypes
		Calibration procedure and organization provision
		Cloud computing alternative: ``fog computing´´
	Conclusion: Trust in ubiquitous medicinal framework
	References
Part 3: Applications of pattern recognition algorithms in U-healthcare
11
PNN-based classification of retinal diseases using fundus images
	Introduction
	Related work
	Methodology
		Database description
		Image preprocessing
			Grayscale conversion
			Multiplication of gray image with vasculature of raw image (retinal vessel mask)
			Subtraction of multiplied image from gray image
			Image dilation
			Image enhancement by contrast stretching
		Feature extraction
			First-order statistics
			Gray-level co-occurrence matrix
			Gray-level run length matrix
		Classification module
	Results and discussion
		Experiment 1: Classification of diabetic retinopathy and glaucoma without image enhancement
		Experiment 2: Classification of diabetic retinopathy and glaucoma using contrast stretching image enhancement
	Conclusion and future scope
	References
12
Performance comparison analysis of different classifier for early detection of knee osteoarthritis
	Introduction
	Analysis of EMG signal
	Principal component analysis
	SVM classifier
	Performance analysis
	Conclusion and future scope
	References
13
A comparative study for brain tumor detection in MRI images using texture features
	Introduction
	Taxonomy and reviews
		First-order histogram-based texture analysis
		Gray-level cooccurrence matrix
		Local binary pattern
		Discrete wavelet transform
	A common experimental protocol
		The dataset
		The classifiers
		Evaluation measures
	A comparative analysis
		Methods selected for comparison
			First-order histogram-based texture analysis
			Gray-level cooccurrence matrix
			Local binary pattern
			Discrete wavelet transform
		Comparative results
	Conclusion
	References
Index
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