Smart Biosensors in Medical Care (Advances in ubiquitous sensing applications for healthcare)

1 - Prototype algorithm for three-class motor imagery data classification: a step toward development of human–computer ...
	1 - Introduction
	2 - Methods
		2.1 - Signal acquisition and preprocessing
			2.1.1 - Signal acquisition
			2.1.2 - Preprocessing
		2.2 - Feature extraction algorithms
			2.2.1 - Linear features
				2.2.1.1 - Band power features
				2.2.1.2 - Power spectral density features
				2.2.1.3 - Features based on maximum power and mean power
			2.2.3 - Nonlinear features
				2.2.3.1 - Approximate entropy
				2.2.3.2 - Correlation coefficient
				2.2.3.3 - Statistical validation of extracted features
		2.3 - Classification of MI-based EEG signals
			2.3.1 - Support vector machine
			2.3.2 - PSO-based SVM
			2.3.3 - Linear discriminant analysis (LDA) and k-nearest neighbor (k-NN)
			2.3.4 - Extreme learning machine (ELM)
		2.4 - Analysis of different classifiers
			2.4.1 - Information transfer rate (ITR)
			2.4.2 - Confusion matrix
	3 - Results from case studies
		3.1 - Preprocessing
		3.2 - Feature extraction for automated classification
		3.3 - Classifiers
	4 - Discussion
	5 - Conclusion
	Acknowledgment
	6 - Compliance with ethical standards
	7 - Conflict of interest
	References
2 - Biosensor and its implementation in diagnosis of infectious diseases
	1 - Introduction
		1.1 - Principle and components of a biosensor
		1.2 - Classification of biosensors
		1.3 - Comparison of current conventional lab techniques vis-a-vis biosensors for disease diagnosis
		1.4 - Computational study of interaction of PfDHFR-TS and PfHDP with heme: promising biomarker targets for malaria
			1.4.1 - Current scenario of malaria diagnosis
	2 - Materials and methods
		2.1 - Determination of three-dimensional structure of PfHDP protein
		2.2 - Docking of PfDHFR-TS and heme
		2.3 - Docking of PfHDP and heme
		2.4 - Structure validation of the modeled structures
	3 - Results and discussion
		3.1 - Binding of heme with PfHDP and PfDHFR-TS
	4 - Conclusion
	References
3 - Biosensors: a better biomarker for diseases diagnosis
	1 - Introduction
	2 - Literature review
		2.1 - Components and basic principle of biosensor
		2.2 - Impact of nanotechnology on biosensor
		2.3 - Biosensor recognition elements
		2.4 - Optical and electrochemical-based biosensors
	3 - Molecular docking study
		3.1 - Results and discussion
	4 - Conclusion
	References
4 - Smart biosensors for an efficient point of care (PoC) health management
	1 - Introduction to biosensors
		1.1 - A biosensor system: receptor–transducer relationship
		1.2 - Kinds of biological receptors
		1.3 - Kinds of biological transducers
			1.3.1 - Electrochemical biotransducers
			1.3.2 - Ion-channel switch biotransducers
			1.3.3 - Reagentless fluorescent biotransducers
	2 - Types of biosensors
		2.1 - Electrochemical biosensors
		2.2 - Optical biosensors
		2.3 - Electrical biosensors
		2.4 - Piezoelectric biosensors
		2.5 - Thermometric biosensors
	3 - Applications of biosensors
		3.1 - Detection of heavy materials
		3.2 - Biotechnology, fermentation, and food industry
		3.3 - Microbial detection in environment
		3.4 - Ozone biosensing
		3.5 - Point-of-care (PoC) monitoring
			3.5.1 - Blood-glucose-level measurement
			3.5.2 - Interferometric reflectance imaging (IRIS)
			3.5.3 - DNA biosensors for forensic/biomedical and agricultural research
			3.5.4 - Tumor Identification
			3.5.5 - Medicine dosage management
			3.5.6 - ECG regulation
			3.5.7 - Menstruation and fertility maintenance
			3.5.8 - Ocular and skin disorders
	4 - Advantages and limitations of smart biosensors in PoC well-being
	5 - Past ventures of biosensors
	6 - Present availability for biosensors
	7 - Future prospects for biosensors
	8 - Discussion
	9 - Conclusion
	Acknowledgments
	References
5 - Energy harvesting via human body activities
	1 - Introduction
		1.1 - Wireless body area network and its applications
		1.2 - Energy harvesting from human body
	2 - Basic blocks of energy harvesting
	3 - Basic energy harvesting principle
		3.1 - Kinetic energy
		3.2 - Biological fuel cells (bio-FCs)
		3.3 - Thermal energy
	4 - Energy potential of a human body from several activities: motivation
	5 - Human energy expenditure from several activities
	6 - Methods of extracting energy from body activities and their power consumptions: literature review
		6.1 - Kinetic energy harvesting methods
		6.2 - Biochemical energy harvesting methods
		6.3 - Thermal energy harvesting methods
		6.4 - Power consumption and data rates of IMDs
	7 - Analysis of power consumption from different sensor devices
	8 - Conclusion
	References
6 - BER performance comparison of EEG healthcare system using 8-pixel, 16-pixel OLED screen, and DSLR camera
	1 - Introduction
	2 - VLC in healthcare
		2.1 - VLC system
		2.2 - Receivers in VLC system
			2.2.1 - Photodetector
			2.2.2 - Imaging sensor or camera—also called a camera sensor
		2.3 - Related work on healthcare system using VLC
		2.4 - VLC in healthcare—state of the art
			2.4.1 - Pukyong National University, South Korea
			2.4.2 - National Taiwan University, Graduate Institute of Photonics and Optoelectronics
			2.4.3 - Moriya Research Laboratory, NTT Communication Science Laboratories, Kyoto, Japan
		2.5 - Visible light optical camera communication
	3 - Methodology of EEG VL-OCC healthcare
	4 - Experimental work and hardware description
	5 - Results and discussion
	6 - Conclusions
	References
7 - FBCC protocol for HBAN network for smart e-health application
	1 - Introduction
	2 - Literature review
	3 - Available algorithms
		3.1 - Queue based
		3.2 - Flow based
	4 - Fuzzy logic
		4.1 - Fuzzy logic-based congestion control (FBCC) protocol
	5 - Application of FBCC in HBAN system for smart e-health application using biosensors
	6 - Conclusion
	References
8 - Biofabrication of graphene QDs as a fluorescent nanosensor for detection of toxic and heavy metals in biological an...
	1 - Introduction
	2 - Experiment
	3 - Results and discussion
		3.1 - The mechanism of binding of cadmium ion by N-GQDs
		3.2 - The selectivity of the chemosensor for Cd(II) detection
		3.3 - Optimization of GQDs as a fluorescence sensor for detection of ions
	4 - Conclusion
	References
9 - Machine learning analysis of topic modeling re-ranking of clinical records
	1 - Introduction
	2 - Related work
	3 - Health and medical topic modeling
	4 - Framework
		4.1 - Patient reports
		4.2 - Dataset
		4.3 - Preprocessing
		4.4 - LDA model
		4.5 - The distribution of topics
	5 - Experiment analysis
		5.1 - Extracting and visualizing topics
			5.1.1 - Extraction of topics by using latent Dirichlet allocation
			5.1.2 - Categorization of disease symptom categorization
				5.1.2.1 - Without symptom-interdependency models
				5.1.2.2 - With immanent symptom interdependency models
				5.1.2.3 - With transcendent term interdependency models
		5.2 - Re-ranking of keyword
		5.3 - Re-ranking of topics
		5.4 - Clinical reports re-ranking
	6 - Conclusion
	References
Chapter 10 - Smart sensing of medical disorder detection for physically impaired person
	1 - Introduction
	2 - Literature survey
	3 - System design considerations
		3.1 - Data collection
		3.2 - Data aggregation
			3.2.1 - Transmission efficiency
			3.2.2 - Network active time
			3.2.3 - Data consistency
			3.2.4 - Latency
			3.2.5 - Visualization
			3.2.6 - Multi-tenancy
			3.2.7 - Quick response
		3.3 - Data storage
			3.3.1 - Data analysis
			3.3.2 - Scalability
			3.3.3 - Collaboration
		3.4 - Automation
		3.5 - Monitoring
	4 - Proposed system architecture
		4.1 - Sensing system
		4.2 - Communication
		4.3 - Skin conductivity sensor
	5 - Attack in WSN
	6 - Proposed setup
		6.1 - Respiration sensor
			6.1.1 - Sensor principle
			6.1.2 - Sensor placement
		6.2 - Heart rate sensor
			6.2.1 - Operational principle
			6.2.2 - Sensor placement
		6.3 - Skin conductivity
			6.3.1 - Operational principle
			6.3.2 - Sensor placement
		6.4 - Smart stick for blind
	7 - Result and analysis
		7.1 - Normal state
		7.2 - Panic state
		7.3 - Abnormal state
	8 - Remarks
	9 - Future scope
	References
11 - Speech-based automation system for the patient in orthopedic trauma ward
	1 - Introduction
	2 - Related work
	3 - System design
	4 - System implementation
		4.1 - Speech recognition overview
		4.2 - System speech recognition
		4.3 - Hardware requirement
		4.4 - System automation
		4.5 - System monitoring
		4.6 - Results
	5 - Conclusion
	6 - Future work
	References