6,387 research outputs found
A Privacy Preserving Framework for RFID Based Healthcare Systems
RFID (Radio Frequency IDentification) is anticipated to be a core technology that will be used in many practical applications of our life in near future. It has received considerable attention within the healthcare for almost a decade now. The technologyâs promise to efficiently track hospital supplies, medical equipment, medications and patients is an attractive proposition to the healthcare industry. However, the prospect of wide spread use of RFID tags in the healthcare area has also triggered discussions regarding privacy, particularly because RFID data in transit may easily be intercepted and can be send to track its user (owner). In a nutshell, this technology has not really seen its true potential in healthcare industry since privacy concerns raised by the tag bearers are not properly addressed by existing identification techniques. There are two major types of privacy preservation techniques that are required in an RFID based healthcare systemâ(1) a privacy preserving authentication protocol is required while sensing RFID tags for different identification and monitoring purposes, and (2) a privacy preserving access control mechanism is required to restrict unauthorized access of private information while providing healthcare services using the tag ID. In this paper, we propose a framework (PriSens-HSAC) that makes an effort to address the above mentioned two privacy issues. To the best of our knowledge, it is the first framework to provide increased privacy in RFID based healthcare systems, using RFID authentication along with access control technique
360 Quantified Self
Wearable devices with a wide range of sensors have contributed to the rise of
the Quantified Self movement, where individuals log everything ranging from the
number of steps they have taken, to their heart rate, to their sleeping
patterns. Sensors do not, however, typically sense the social and ambient
environment of the users, such as general life style attributes or information
about their social network. This means that the users themselves, and the
medical practitioners, privy to the wearable sensor data, only have a narrow
view of the individual, limited mainly to certain aspects of their physical
condition.
In this paper we describe a number of use cases for how social media can be
used to complement the check-up data and those from sensors to gain a more
holistic view on individuals' health, a perspective we call the 360 Quantified
Self. Health-related information can be obtained from sources as diverse as
food photo sharing, location check-ins, or profile pictures. Additionally,
information from a person's ego network can shed light on the social dimension
of wellbeing which is widely acknowledged to be of utmost importance, even
though they are currently rarely used for medical diagnosis. We articulate a
long-term vision describing the desirable list of technical advances and
variety of data to achieve an integrated system encompassing Electronic Health
Records (EHR), data from wearable devices, alongside information derived from
social media data.Comment: QCRI Technical Repor
M-health review: joining up healthcare in a wireless world
In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint
Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review
Internet of Things (IoT) is an evolution of the Internet and has been gaining increased
attention from researchers in both academic and industrial environments. Successive technological
enhancements make the development of intelligent systems with a high capacity for communication
and data collection possible, providing several opportunities for numerous IoT applications,
particularly healthcare systems. Despite all the advantages, there are still several open issues
that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information
security, and privacy. IoT provides important characteristics to healthcare systems, such as availability,
mobility, and scalability, that o er an architectural basis for numerous high technological healthcare
applications, such as real-time patient monitoring, environmental and indoor quality monitoring,
and ubiquitous and pervasive information access that benefits health professionals and patients.
The constant scientific innovations make it possible to develop IoT devices through countless services
for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced
living environments (ELEs). This paper reviews the current state of the art on IoT architectures for
ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities,
open-source platforms, and operating systems. Furthermore, this document synthesizes the existing
body of knowledge and identifies common threads and gaps that open up new significant and
challenging future research directions.info:eu-repo/semantics/publishedVersio
Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing
In this paper, a first approach to the design of a portable device for non-contact monitoring
of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart
vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive
Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation
exercises at home. To provide an extensible solution to the remote monitoring using this sensor and
other devices, the design and preliminary development of an e-Health platform based on the Internet
of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution,
two quasi-experimental studies have been developed, comparing the estimations with respect to the
golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error,
the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm),
0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values
were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period
show the technical and functional feasibility of the prototype and serve as a preliminary validation of
the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e InnovaciĂłn PI15/00306Ministerio de Ciencia e InnovaciĂłn DTS15/00195Junta de AndalucĂa PI-0010-2013Junta de AndalucĂa PI-0041-2014Junta de AndalucĂa PIN-0394-201
Wireless Medical Sensor Networks: Design Requirements and Enabling Technologies
This article analyzes wireless communication protocols that could be used in healthcare environments (e.g., hospitals and small clinics) to transfer real-time medical information obtained from noninvasive sensors. For this purpose the features of the three currently most widely used protocolsânamely, BluetoothÂź (IEEE 802.15.1), ZigBee (IEEE 802.15.4), and Wi-Fi (IEEE 802.11)âare evaluated and compared. The important features under consideration include data bandwidth, frequency band, maximum transmission distance, encryption and authentication methods, power consumption, and current applications. In addition, an overview of network requirements with respect to medical sensor features, patient safety and patient data privacy, quality of service, and interoperability between other sensors is briefly presented. Sensor power consumption is also discussed because it is considered one of the main obstacles for wider adoption of wireless networks in medical applications. The outcome of this assessment will be a useful tool in the hands of biomedical engineering researchers. It will provide parameters to select the most effective combination of protocols to implement a specific wireless network of noninvasive medical sensors to monitor patients remotely in the hospital or at home
Self-managed cells and their federation
Future e-Health systems will consist of low-power, on-body wireless sensors attached to mobile users that interact with a ubiquitous computing environment. This kind of system needs to be able to configure itself with little or no user input; more importantly, it is required to adapt autonomously to changes such as user movement, device failure, the addition or loss of services, and proximity to other such systems. This extended abstract describes the basic architecture of a Self-Managed Cell (SMC) to address these requirements, and discusses various forms of federation between/among SMCs. This structure is motivated by a typical e-Health scenario
SIMPLE: Stable Increased-throughput Multi-hop Protocol for Link Efficiency in Wireless Body Area Networks
In this work, we propose a reliable, power efficient and high throughput
routing protocol for Wireless Body Area Networks (WBANs). We use multi-hop
topology to achieve minimum energy consumption and longer network lifetime. We
propose a cost function to select parent node or forwarder. Proposed cost
function selects a parent node which has high residual energy and minimum
distance to sink. Residual energy parameter balances the energy consumption
among the sensor nodes while distance parameter ensures successful packet
delivery to sink. Simulation results show that our proposed protocol maximize
the network stability period and nodes stay alive for longer period. Longer
stability period contributes high packet delivery to sink which is major
interest for continuous patient monitoring.Comment: IEEE 8th International Conference on Broadband and Wireless
Computing, Communication and Applications (BWCCA'13), Compiegne, Franc
Wireless body sensor networks for health-monitoring applications
This is an author-created, un-copyedited version of an article accepted for publication in
Physiological Measurement. The publisher is
not responsible for any errors or omissions in this version of the manuscript or any version
derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01
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