Pervasive Health Monitoring Systems Research Articles

Software architecture for pervasive critical health monitoring system using fog computing

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

Pervasive intelligent multi-node health monitoring system for monitoring basketball players health and energy using IoT and 6G technology

Effective health monitoring of players in team sports like basketball allows for understanding external requirements and internal response concerning exercise and competition phases. The explosive growth of wireless devices stimulates the advancement of the internet-of-things (IoT) and 6G technologies, capable of connecting enormous and various “things” through wireless communications. Players face health issues while playing basketball are severe lower body lesions like ankle sprains, shortness of breath, teeth, head, fingers, and hand. To overcome these issues, in this paper, the Pervasive Intelligent Multi-node Health Monitoring System (PIMN-HMS) has been proposed for basketball player’s continuous health tracking based on IoT and 6G communication. With the aid of wearable monitoring sensors to gathers health information and monitor exercise records. The system consists of several sensor nodes, a network coordinator, which monitors physical movements and heart rate, and a personal server on a personal digital assistant using 6G networks. The numerical results have been performed, and the suggested PIMN-HMS model enhances the accuracy ratio of 96.7%, prediction ratio of 97.3%, low latency ratio of 11.2%, delay rate of 22.3%, and efficiency ratio of 98.7% compared to other existing models.

Introduction to the Special Issue on Wearable and Flexible Integrated Sensors for Screening, Diagnostics, and Treatment

The papers in this special issue present a selection of high quality research papers on wearable and flexible integrated sensors for screening, diagnostics, and treatment. Emerging flexible and wearable physical sensing devices create huge potential for many vital healthcare and biomedical applications including artificial electronic skins, physiological monitoring and assessment systems, therapeutic and drug delivery platforms, etc. Monitoring of vital physiological parameters in hospital and/or home environments has been of tremendous interests to healthcare practitioners for a long time. Robust and reliable sensors with excellent flexibility and stretchability are essential in the development of pervasive health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous discomfort and invasiveness.

Toward Securing Cyber-Physical Systems Using Exact Cover Set

Cyber physical systems (CPS) are computer systems that integrate computing, coordination and communication systems in order to monitor physical entities in the physical world. As they interact with the critical systems and infrastructure that may impact real life. There are many issues in design and constructions of these types of systems. One of the key issue that receives so many attention is the security of such systems. Although security solutions for Information Systems handle the security issues associated with Traditional IT security, but the impact of a failure in a cyber-physical system demands a different approach to handle security issues related to the cyber world. In this work, we focus on using a key agreement technique known as Physical Signal Key Agreement (PSKA) technique using the Exact Cover method to generate the random key which will be embedded into an access control model known as the Modified Context-Aware Security The feasibility of our framework (MCASF) to handle both normal and critical situation on demand is demonstrated via a Pervasive Health Monitoring Systems (PHMS).

Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System

The exponentially growing healthcare costs coupled with the increasing interest of patients in receiving care in the comfort of their own homes have prompted a serious need to revolutionize healthcare systems. This has prompted active research in the development of solutions that enable healthcare providers to remotely monitor and evaluate the health of patients in the comfort of their residences. However, existing works lack flexibility, scalability, and energy efficiency. This article presents a pervasive patient health monitoring (PPHM) system infrastructure. PPHM is based on integrated cloud computing and Internet of Things technologies. In order to demonstrate the suitability of the proposed PPHM infrastructure, a case study for real-time monitoring of a patient suffering from congestive heart failure using ECG is presented. Experimental evaluation of the proposed PPHM infrastructure shows that PPHM is a flexible, scalable, and energy-efficient remote patient health monitoring system.

Analysis of Smart Mobile Applications for Healthcare under Dynamic Context Changes

Smart mobile medical computing systems (SMDCSes), e.g., mobile medical applications use context information from the environment to provide useful and often critical healthcare services such as continuous monitoring and control of blood glucose levels by infusion of insulin. Given the unsupervised nature of operation of SMDCSes, context changes that are unaccounted for can cause unprecedented faults leading to violation of requirements such as safety, energy sustainability and reliability. Analysis of SMDCSes for testing requirements violations necessitates consideration of context dependent interactions between the SMDCS software, represented by discrete operating modes and its environment, represented by non-linear partial differential equations over space and time. An intractable number of context change sequence and lack of closed form solutions to differential equations makes the requirements analysis of SMDCSes a challenging task. This paper proposes a novel technique to analyze SMDCSes taking into account the dynamic changes in the context and the constant interaction of the computing systems with the physical environment. To show the usage of the technique, Ayushman pervasive health monitoring system is considered as an example SMDCS. Analytical results show that practices considered healthy for a person such as mobility may not be beneficial when an SMDCS is controlling health.

Optimal Resource Allocation for Pervasive Health Monitoring Systems with Body Sensor Networks

Pervasive health monitoring is an eHealth service, which plays an important role in prevention and early detection of diseases. There are two major challenges in pervasive health monitoring systems with Body Sensor Networks (BSNs). The first challenge is the sustainable power supply for BSNs. The second challenge is Quality of Service (QoS) guarantee for the delivery of data streams. In this paper, we optimize the resource allocations to provide a sustainable and high-quality service in health monitoring systems. Specifically, we formulate and solve two resource optimization problems, respectively. In the first optimization problem, steady-rate optimization problem, we optimize the source rate at each sensor to minimize the rate fluctuation with respect to the average sustainable rate, subject to the requirement of uninterrupted service. The first optimization problem is solved by a proposed analytical solution. The second optimization problem is formulated based on the optimal source rates of the sensors obtained in the steady-rate optimization problem. In the second optimization problem, we jointly optimize the transmission power and the transmission rate at each aggregator to provide QoS guarantee to data delivery. The second optimization problem is converted into a convex optimization problem, which is then solved efficiently. In the simulations, we demonstrate that the proposed optimized scheme enables the pervasive health monitoring system to provide a sustainable service with guaranteed low delay and low Packet Loss Rate (PLR) to subscribers.

Sensor network security for pervasive e-health

Ubiquitous 24/7 health monitoring systems based on wireless medical sensors are going to play a key role for pervasive e-health applications. These systems allow care givers to early detect and act on signs of patients' clinical deterioration improving quality of care in a reliable unobtrusive and cost effective way. Ensuring the privacy and security of the exchanged information is challenging in pervasive e-health environments due to the resource constraints of tiny wireless medical sensors and operational requirements such as user mobility, strict latency needs, or the multitude of parties involved in the system. This paper describes a comprehensive and practical security framework for these pervasive health monitoring systems. We distinguish three layers addressing the specific security needs at the patient area network, medical sensor network, and back-end levels. Thereby our architecture accommodates the healthcare institution-centric approach predominant today while making provisions for the more patient-centric vision of pervasive e-health environments. The tailored security mechanisms for each individual layer as well as their interworking are presented and evaluated in detail. The analysis shows that our proposed security framework allows the deployment of wireless medical sensor networks in a very efficient way. Copyright © 2010 John Wiley & Sons, Ltd.

Enhancing Battery Efficiency for Pervasive Health-Monitoring Systems Based on Electronic Textiles

Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.