A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted

A Survey of Insulin-Dependent Diabetes—Part II: Control Methods

We survey blood glucose control schemes for insulin-dependent diabetes therapies and systems. These schemes largely rely on mathematical models of the insulin-glucose relations, and these models are typically derived in an empirical or fundamental way. In an empirical way, the experimental insulin inputs and resulting blood-glucose outputs are used to generate a mathematical model, which includes a couple of equations approximating a very complex system. On the other hand, the insulin-glucose relation is also explained from the well-known facts of other biological mechanisms. Since these mechanisms are more or less related with each other, a mathematical model of the insulin-glucose system can be derived from these surrounding relations. This kind of method of the mathematical model derivation is called a fundamental method. Along with several mathematical models, researchers develop autonomous systems whether they involve medical devices or not to compensate metabolic disorders and these autonomous systems employ their own control methods. Basically, in insulin-dependent diabetes therapies, control methods are classified into three categories: open-loop, closed-loop, and partially closed-loop controls. The main difference among these methods is how much the systems are open to the outside people

mHealth: A Utilization Review by Feature Classification for Sustained Use

mHealth is a fast growing segment for healthcare. However, there has been little research into the specific elements of mHealth that can drive continued use for optimization of the potential benefits. The purpose of this case study was to use the Delone and McLean Information System Model as a framework for classification of mHealth functionality and then to review the utilization of those categories over a six month period of time. A sample of 137 pediatric diabetics was reviewed. The activation rate was high at 94.9% indicating an interest in using mHealth. There was higher utilization of system features in the group of users with 60.3% of total uses being related to a system feature. There also were specific use patterns between gender with male patients consisting of 66.2% of the overall uses. Future applications should focus on system features and customization by gender to support sustained use

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Modelling Glucose Regulatory System: Adaptive System Dynamic Approach

Thesis describes a project that starts from evaluation of simulation programs and ends to testing an individually adaptive glucose regulatory system (GRS) model. Thesis presents a modern adaptive approach to model GRS in order to describe each diabetic’s individual causalities. Thesis is divided into three parts; a literature study of diabetes and GRS models, analysis of simulation programs, and building dynamics GRS model and validating it with clinical data. Validation consists literature data for general GRS model and test data from a pilot diabetic who underwent two-week study period. Data collected included glucose values from two continuous glucose monitors (CGM), fingertip blood glucose measurements, meals and exercises. Adaptive parameter identification was applied to the model during 6 days training period and then blood glucose was estimated for the next 24 hours. First part of results show that from four simulation programs analyzed, Simulink was the software best meeting Quattro Folia’s functional requirements and demanded qualities. Therefore, a general GRS model was built with it. Based on literature review, the best model and parts of models were combined for one general model which was validated to function as in previous studies. Second part of results show that with adequate data, blood glucose can be estimated with decent accuracy. Although the material only consist data from one diabetic subject, it gives an indication that blood glucose could be estimated for others also. However, the precision over population is indecisive. To conclude, individual diabetic’s GRS and its functions can be described with adaptive system dynamic model. The model have multiple possible usages from in silico testing to teaching causalities for diabetics or their parents, thus it is useful for research, validation and educational purposes. Its value creators are modularity and wide range of possible usages

Clinical Prediction on ML based Internet of Things for E-Health Care System

Machine learning (ML) is a powerful method for uncovering hidden patterns in data from the Internet of Things (IoT). These hybrid solutions intelligently improve decision-making in a variety of fields, including education, security, business, and healthcare. IoT uses machine learning to uncover hidden patterns in bulk data, allowing for better forecasting and referral systems. IoT and machine learning have been embraced in healthcare so that automated computers may generate medical records, anticipate diagnoses, and, most critically, monitor patients in real time. On different databases, different ML algorithms work differently. The overall outcomes may be influenced by the variance in anticipated results. In the clinical decision-making process, there is a lot of variation in prognostic results. As a result, it's critical to comprehend the various machine learning methods utilised to handle IoT data in the healthcare industry. Machine learning of adaptive neuro fuzzy inference system (ANFIS) algorithms is being used to monitor human health in this suggested effort. The UCI database is used for initial training and validation of machine learning systems. Using the IoT system, the test phase collects the person's heart rate, blood pressure, and temperature. The test stage assesses if the sensor data obtained by the IoT framework can predict any irregularities in the health state. To evaluate the accuracy of the forecast %, statistical analysis is performed on cloud data acquired from the IoT. Other routines are derived from K-neighbour results

Model-Based Analysis of User Behaviors in Medical Cyber-Physical Systems

Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis. This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., non-personalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies