RFID antennas implanted for pervasive healthcare applications

2016 ◽  
Author(s):  
Nada Jebali ◽  
Sabri Beldi ◽  
Ali Gharsallah
Keyword(s):  
Healthcare Applications ◽  
Pervasive Healthcare ◽  
Rfid Antennas

Communication Issues in Pervasive Healthcare Systems and Applications

2010 ◽  
pp. 197-227 ◽  
Author(s):  
Demosthenes Vouyioukas ◽  
Ilias Maglogiannis
Keyword(s):  
Healthcare Delivery ◽  
Communication Technologies ◽  
Mobile Technologies ◽  
Future Trends ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Systematic Analysis ◽  
Pervasive Healthcare ◽  
Body Sensors

This book chapter provides a systematic analysis of the communication technologies used in healthcare and homecare, their applications and the utilization of the mobile technologies in the healthcare sector by using in addition case studies to highlight the successes and concerns of homecare projects. There are several software applications, appliances, and communication technologies emerging in the homecare arena, which can be combined in order to create a pervasive mobile health system. This study highlights the key areas of concern and describes various types of applications in terms of communications’ performance. A comprehensive overview of some of these homecare, healthcare applications and research are presented. The technologies regarding the provision of these systems are described and categorised in two main groups: synchronous and asynchronous communications’ systems and technologies. The recent advances in homecare using wireless body sensors and on/off-body networks technologies are discussed along with the provision of future trends for pervasive healthcare delivery. Finally, this book chapter ends with a brief discussion and concluding remarks in succession to the future trends.

A Framework for Capturing Patient Consent in Pervasive Healthcare Applications

2011 ◽  
pp. 659-673
Author(s):  
Giovanni Russello ◽  
Changyu Dong ◽  
Naranker Dualy
Keyword(s):  
Medical Data ◽  
Pivotal Role ◽  
Actual Situation ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Pervasive Healthcare ◽  
Patient Consent ◽  
Access Rights ◽  
New Framework

In this chapter, the authors describe a new framework for pervasive healthcare applications where the patient’s consent has a pivotal role. In their framework, patients are able to control the disclosure of their medical data. The patient’s consent is implicitly captured by the context in which his or her medical data is being accessed. Context is expressed in terms of workflows. The execution of a task in a workflow carries information that the system uses for providing access rights accordingly to the patient’s consent. Ultimately, the patient is in charge of withdrawing consent if necessary. Moreover, the use of workflow enables the enforcement of the need-to-kwon principle. This means that a subject is authorised to access sensitive data only when required by the actual situation.

A Framework for Capturing Patient Consent in Pervasive Healthcare Applications

2010 ◽  
pp. 245-259
Author(s):  
Giovanni Russello ◽  
Changyu Dong ◽  
Naranker Dualy
Keyword(s):  
Medical Data ◽  
Pivotal Role ◽  
Actual Situation ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Pervasive Healthcare ◽  
Patient Consent ◽  
Access Rights ◽  
New Framework

In this chapter, the authors describe a new framework for pervasive healthcare applications where the patient’s consent has a pivotal role. In their framework, patients are able to control the disclosure of their medical data. The patient’s consent is implicitly captured by the context in which his or her medical data is being accessed. Context is expressed in terms of workflows. The execution of a task in a workflow carries information that the system uses for providing access rights accordingly to the patient’s consent. Ultimately, the patient is in charge of withdrawing consent if necessary. Moreover, the use of workflow enables the enforcement of the need-to-kwon principle. This means that a subject is authorised to access sensitive data only when required by the actual situation.

Model-Driven Prototyping Support for Pervasive Healthcare Applications

2010 ◽  
pp. 251-281
Author(s):  
Werner Kurschl ◽  
Stefan Mitsch ◽  
Johannes Schoenboeck
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Application Development ◽  
Healthcare Applications ◽  
Development Environment ◽  
Pervasive Healthcare ◽  
Model Driven

Pervasive healthcare applications aim at improving habitability by assisting individuals in living autonomously. To achieve this goal, data on an individual’s behavior and his or her environment (often collected with wireless sensors) is interpreted by machine learning algorithms; their decision finally leads to the initiation of appropriate actions, e.g., turning on the light. Developers of pervasive healthcare applications therefore face complexity stemming, amongst others, from different types of environmental and vital parameters, heterogeneous sensor platforms, unreliable network connections, as well as from different programming languages. Moreover, developing such applications often includes extensive prototyping work to collect large amounts of training data to optimize the machine learning algorithms. In this chapter the authors present a model-driven prototyping approach for the development of pervasive healthcare applications to leverage the complexity incurred in developing prototypes and applications. They support the approach with a development environment that simplifies application development with graphical editors, code generators, and pre-defined components.

Identifying RFID-embedded objects in pervasive healthcare applications

2009 ◽  
Vol 46 (2) ◽  
pp. 586-593 ◽  
Author(s):  
Yu-Ju Tu ◽  
Wei Zhou ◽  
Selwyn Piramuthu
Keyword(s):  
Healthcare Applications ◽  
Pervasive Healthcare

DISTRIBUTED TELEMATICS PLATFORM FOR PERVASIVE HEALTHCARE APPLICATIONS

2003 ◽  
Vol 48 (s1) ◽  
pp. 140-141
Author(s):  
C. A. Kunze ◽  
U. Grossmann ◽  
J. Ottenbacher ◽  
W. Stork ◽  
K.D. Müller-Glaser
Keyword(s):  
Healthcare Applications ◽  
Pervasive Healthcare

scholarly journals A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 367
Author(s):  
Hina Magsi ◽  
Ali Hassan Sodhro ◽  
Mabrook S. Al-Rakhami ◽  
Noman Zahid ◽  
Sandeep Pirbhulal ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Sensor Nodes ◽  
The Internet ◽  
Healthcare Applications ◽  
Pervasive Healthcare ◽  
Battery Lifetime ◽  
Linear Behavior ◽  
Main Challenge ◽  
Finite State ◽  
Multiple Resource

The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

Intelligent Health Monitoring Based on Pervasive Technologies and Cloud Computing

2014 ◽  
Vol 23 (03) ◽  
pp. 1460001 ◽  
Author(s):  
Ilias Maglogiannis ◽  
Charalampos Doukas
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Healthcare Applications ◽  
Pervasive Healthcare ◽  
Health Monitoring System ◽  
User Acceptability ◽  
Acquisition Management ◽  
Initial Results

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising.

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