Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

DETEKSI DIABETES MELITUS UNTUK WANITA DAN PENYUSUNAN MENU SEHAT DENGAN PENDEKATAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DAN ALGORITMA GENETIKA (GA)

Diabetes melitus (DM) merupakan salah satu penyakit kronis (menahun) yang disebabkan berkurangnya produksi insulin dari pankreas maupun insulin yang dihasilkan tidak efektif dalam mengurangi kadar gula darah. Keadaan ini akan meningkatkan kadar gula darah sehingga merusak sistem kekebalan tubuh.  Penanganan awal pada penderita DM adalah dengan mengubah gaya hidup yaitu mengkonsumsi makanan dengan kandungan nutrisi yang diperlukan oleh tubuh dan memperbanyak aktivitas fisik. Untuk mengatur pola makan  pada penderita DM maka diperlukan diet dengan mengatur komposisi pola makanan dan mengendalikan kadar gula darah. Penelitian ini bertujuan untuk membuat suatu model penyusunan menu makanan sehat berdasarkan  jumlah kebutuhan kalori per hari, sehingga memenuhi kriteria gizi seimbang dan memenuhi variasi makanan berupa makanan pokok, lauk pauk, sayuran dan buah. Pada penelitian ini metode Adaptive Neuro Fuzzy Inference System (ANFIS) dan Algoritma Genetika (GA) digunakan untuk memberikan saran penyajian  makanan yang memenuhi jenis menu dan jumlah porsi yang ideal bagi penderita DM. Hasil penelitian menunjukkan bahwa metode yang diusulkan memperoleh nilai untuk accuracy training sebesar 89.1% dengan menggunakan metode ANFIS dan untuk pemenuhan nutrisi yang dicapai sebesar 98.9% dengan menggunakan GA yang artinya bahwa metode ANFIS dan GA dapat memberikan hasil akhir yang sangat baik yaitu dengan menghasilkan menu sehat yang memenuhi gizi yang optimal dan tercapainya keanekaragaman makanan sesuai dengan 4 pilar gizi seimbang.Kata kunci :Diabetes Melitus, ANFIS, Algoritma Genetika, menu sehat. Diabetes mellitus (DM) is one chronic disease caused by reduced production of insulin from the pancreas and insulin produced is not effective in reducing blood sugar levels. This situation will increase blood sugar levels, thus damaging the immune system. Initial treatment in diabetics is to change the lifestyle of eating foods with nutritional content needed by the body and increase physical activity. To regulate the diet in people with diabetes melitus, it takes the diet by adjusting the composition of diet patterns and control blood sugar levels. This study aims to create a healthy food menu based on the number of caloric needs per day, so that it meets the criteria of balanced nutrition and meets the variety of foods in the form of main dishes, side dishes, vegetables and fruits. In this research, Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) method are used to provide suggestions for serving foods that meet the menu type and ideal portion for DM patients. The results showed that the proposed method scored 89.1% accuracy training by using ANFIS method and for fulfillment of nutrition reached 98.9% by using GA which means that ANFIS and GA method can give excellent result that is by producing Healthy food menu that meets optimal nutrition and achievement of food diversity in accordance with 4 pillars of balanced nutrition. Keyword :Diabetes Melitus, ANFIS, Genetic Alghorithm, healthy menu.

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals

Energy efficiency considerations in software‐defined wireless body area networks

Wireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software-defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy-efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software-defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare

Immaterial Attachments: Performing iPhone and the Rhetorics of Dematerialization

Engaging with rhetorical studies, performance studies, and surveillance studies, this thesis attempts to outline the ideological construction of the experience of iPhone, underlining how this experience—and its performance—is imbricated with conceptions of social control. To do this, I begin with the cultural oscillation between extreme psychological attachment to Apple’s iPhone and its complementary disposability. How can an object generate such attachment, yet remain disposable? To get at this question, I examine how attachment and disposability are layered together in an experience of iPhone structured by rhetorics of dematerialization. These are visual and discursive fragments that, together, construct an ideological impulse that tends toward the disappearance of the objects to which they refer, overall, working to supplement and promote iPhone’s culture of disposability. In relation to iPhone, this thesis examines rhetorics of dematerialization through three intersecting vectors: the device, the human user, and the proximal space that stages their interaction. With rhetorics of dematerialization as the larger frame, my main analyses focus on specific instances of the tension between attachment and disposability, considered as performances of attachment. Generally, these are everyday performances on and with iPhone—gestural interface, picking it up, throwing it out—that 1) collapse attachment and disposability into each other under the rhetorical rubric of a phenomenal dematerialization, 2) require users to enact, embody, and assume the rhetorics of dematerialization, and 3) have both cultural and individual effects. iPhone’s culture of disposability relies on the dematerialization of waste and wasteful consumer practices. Individually, performances of attachment with iPhone allow new models of surveillance (through data-gathering and self-tracking practices) to permeate users’ everyday experience

Network Function Virtualization Technology Adoption Strategies

Network function virtualization (NFV) is a novel system adopted by service providers and organizations, which has become a critical organizational success factor. Chief information officers (CIOs) aim to adopt NFV to consolidate and optimize network processes unavailable in conventional methods. Grounded in the diffusion of innovation theory (DOI), the purpose of this multiple case research study was to explore strategies chief information officers utilized to adopt NFV technology. Participants include two CIOs, one chief security information officer (CSIO), one chief technical officer (CTO), and two senior information technology (IT) executives. Data were collected through semi-structured telephone interviews and eight organizational documents. Through thematic analysis, four significant themes became apparent: organizational awareness, no hindrances to NFV technology adoption, documentation and implementation plan, and operational costs and efficiency. A key recommendation is for CIOs, CSIOs, CTOs, and senior IT managers to adopt the capability to document globally accepted processes and procedures for seamless adoption of NFV technology. The implications for positive social change include the potential to reduce energy consumption, preserving natural resources, and reducing environmental pollution due to the emission of dangerous gases that cause environmental degradation

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications