Healthcare monitoring

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the...

The wearable electrocardiogram (W-ECG) signal inherently contains motion artifacts due to various body movements of the wearer. The W-ECG signals with four body movement activit ies (BMAs) - left arm up-down, right arm up-down, waist-twist and walking of five healthy subjects have been acquired using the wearable ECG recorder. The classification of these four BMAs has been performed using artificial neural networks (A NN). In the process, the motion artifacts contained in the captured W-ECG signals have been extracted using Wavelet transform and the features of the motion artifacts have been extracted using Gabor transform. These feature vectors are fed to a mu lti-layered perceptron neural network (M LPNN) consisting of ten neuron hidden layer. The overall classification accuracy achieved using ANN is close to 92%.

Progress on smart self-powered electronic skin (e-skin) presents unique opportunities to detect and discriminate static human physiological signals and dynamic tactile stimuli. Nevertheless, development of piezoelectric materials possessing adequate flexibility, light weight, ease of large-area processing, low cost and environmental safety are attractive but remains a challenging choice for next-generation pressure/force sensors and mechanical energy harvesters. Here, we design a wearable bio-inspired piezoelectric pressure sensor (i.e., bio-e-skin) from structurally stable fish gelatin nanofibers (GNFs) using large area compatible electrospining technology. Owing to superior mechanosensitivity (~0.8 V kPa −1), the bio-e-skin can mimic spatiotemporal human perception and monitors real-time human physiological signals in non-invasive rational strategy. More importantly, nanoscale ferro– and piezo–electricity (d 33 ~−20 pm/V) in GNFs, realized by piezoresponse force microscopy allow the bio-e-skin to be self-powered with excellent operational stability (over 108,000 cycles) and anti-fatigue (over 6 months) properties which solve the complication of external power supply for pressure sensing applications.

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the...

A human interactive self-powered wearable sensor is designed using waste by-product prawn shells. The structural origin of intrinsic piezoelectric characteristics of bio-assembled chitin nanofibers
has been investigated. It allows the prawn shell to make a tactile sensor that performs also as a highly durable mechanical energy harvester/nanogenerator. The feasibility and fundamental physics
of self-powered consumer electronics even from human perception is highlighted by prawn shells made nanogenerator (PSNG). High fidelity and non-invasive monitoring of vital signs, such as radial artery pulse wave and coughing actions, may lead to the potential use of PSNG for early intervention. It is presumed that PSNG has enormous future aspects in real-time as well as remote health care assessment.

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the...

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the...

Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the IP and optical layers is proposed, and an application for detecting DoS attacks is introduced. The proposed application is a lightweight implementation that could be applied and installed into various remote healthcare control devices with limited processing and memory resources. Finally, an analytical and focused approach correlated to attack detection is proposed, which can also serve as a tutorial oriented towards even nonprofessionals for practical and learning purposes.