Handbook of Large-Scale Distributed Computing in Smart Healthcare

Inhaltsverzeichnis

1. Frontmatter

2. Introduction to Large-Scale Distributed Computing in Smart Healthcare

   Assad Abbas, Samee U. Khan, Albert Y. Zomaya

   Abstract

   Conventional healthcare services have seamlessly been integrated with the pervasive computing paradigm and consequently cost-effective and dependable smart healthcare services and systems have emerged [1].

3. High Performance Computing and Large-Scale Healthcare Architectures

   1. Frontmatter

   2. Big Healthcare Data Analytics: Challenges and Applications

      Chonho Lee, Zhaojing Luo, Kee Yuan Ngiam, Meihui Zhang, Kaiping Zheng, Gang Chen, Beng Chin Ooi, Wei Luen James Yip

      Abstract

      Increasing demand and costs for healthcare, exacerbated by ageing populations and a great shortage of doctors, are serious concerns worldwide. Consequently, this has generated a great amount of motivation in providing better healthcare through smarter healthcare systems. Management and processing of healthcare data are challenging due to various factors that are inherent in the data itself such as high-dimensionality, irregularity and sparsity. A long stream of research has been proposed to address these problems and provide more efficient and scalable healthcare systems and solutions. In this chapter, we shall examine the challenges in designing algorithms and systems for healthcare analytics and applications, followed by a survey on various relevant solutions. We shall also discuss next-generation healthcare applications, services and systems, that are related to big healthcare data analytics.

   3. Process Streaming Healthcare Data with Adaptive MapReduce Framework

      Fan Zhang, Junwei Cao, Samee U. Khan, Keqin Li, Kai Hwang

      Abstract

      As one of the most widely used healthcare scientific applications, body area network with hundreds of interconnected sensors need to be used to monitor the health status of a physical body. It is very challenging to process, analyze and monitor the streaming data in real time. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real-time streaming data applications. The state-of-art cloud platform either lacks of such capability to process highly concurrent streaming data, or scales in regards to coarse-grained compute nodes. In this chapter, we propose a task-level adaptive MapReduce framework. This framework extends the generic MapReduce architecture by designing each Map and Reduce task as a scalable daemon process. The beauty of this new framework is the scaling capability being designed at the Map and Reduce task level, rather than being scaled at the compute-node level, as traditional MapReduce does. This design is capable of not only scaling up and down in real time, but also leading to effective use of compute resources in cloud data center. As a first step towards implementing this framework in real cloud, we have developed a simulator that captures workload strength, and provisions the just-in-need amount of Map and Reduce tasks in realtime. To further enhance the framework, we applied two streaming data workload prediction methods, smoothing and Kalman filter, to estimate the workload characteristics. We see 63.1% performance improvement by using the Kalman filter method to predict the workload. We also use real streaming data workload trace to test the framework. Experimental results show that this framework schedules the Map and Reduce tasks very efficiently, as the streaming data changes its arrival rate.

   4. High-Performance Monte Carlo Simulations for Photon Migration and Applications in Optical Brain Functional Imaging

      Fanny Nina-Paravecino, Leiming Yu, Qianqian Fang, David Kaeli

      Abstract

      The human brain is a complex biological organ that is extremely challenging to study. Non-invasive optical scanning has been effective in exploring brain functions and diagnosing brain diseases. However, given the complexity of the human brain anatomy, quantitative analysis of optical brain imaging data has been challenging due to the extensive computation needed to solve the generalized models. In this chapter, we discuss Monte Carlo eXtreme (MCX), a computationally efficient and numerically accurate Monte Carlo photon simulation package. Leveraging the benefits of GPU-based parallel computing. MCX allows researchers to use 3D anatomical scans from MRI or CT to perform accurate photon transport simulations. Compared to conventional Monte Carlo (MC) methods, MCX provides a dramatic speed improvement of two to three orders of magnitude, thanks largely to the massively parallel threads enabled by modern GPU architectures.

   5. Building Automation and Control Systems for Healthcare in Smart Homes

      M. Frenken, J. Flessner, J. Hurka

      Abstract

      The chapter presents an overview of building control systems addressing healthcare issues in home environments. Main goal of a building control installation is to ensure energy efficiency and comfort in home or functional buildings. Nevertheless, recent scientific work on the design of building control systems focuses on the inhabitants’ state of health. The chapter starts with a definition and distinction of several synonymously used terms in the field of building automation and control systems (BACS) and a general architectural design. Section 2 introduces a classification of health related applications using BACS while Sect. 3 gives selected examples for each class to give an overview about possibilities, limits and efforts on the creation of building environments with positive health effects using building control systems. Those example applications will range from adaptive lighting control for health treatment (e.g. in dementia, or depression) to smart home automation networks for activity recognition. Each mentioned system is aiming to create an improved environment, support healthy living, or to detect emergencies and to react adequately. Finally, the achievements of recent scientific works are summarized and recommendations for the development of even more adaptive and healthier building environments through distributed building system technologies are discussed.

4. Data Quality and Large-Scale Machine Learning Models for Smart Healthcare

   1. Frontmatter

   2. Electronic Health Records: Benefits and Challenges for Data Quality

      Abdul Kader Saiod, Darelle van Greunen, Alida Veldsman

      Abstract

      Data quality (DQ) issues in Electronic Health Records (EHRs) are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards to large-scale health Database Management Systems (DBMS). In addition, EHR technology provides portfolio management systems that allow Health Care Organisations (HCOs) to deliver higher quality of care to their patients than possible with paper-based records. The EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. An efficient EHRs system reduces data redundancy as well as system application failures and increases the possibility to draw all necessary reports. Improving DQ to achieve benefits through EHRs is neither low-cost nor easy. However, different HCOs have several standards and different major systems, which have emerged as critical issues and practical challenges. One of the main challenges in EHRs is the inherent difficulty to coherently manage incompatible and sometimes inconsistent data structures from diverse heterogeneous sources. As a result, the interventions to overcome these barriers and challenges, including the provision of EHRs as it pertains to DQ will combine features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets.

   3. Large Scale Medical Data Mining for Accurate Diagnosis: A Blueprint

      Md. Sarwar Kamal, Nilanjan Dey, Amira S. Ashour

      Abstract

      Medical care and machine learning are associated together in the current era. For example, machine learning (ML) techniques support the medical diagnosis process/decision making on large scale of diseases. Advanced data mining techniques in diseases information processing context become essential. The present study covered several aspects of large scale knowledge mining for medical and diseases investigation. A genome-wide association study was reported including the interactions and relationships for the Alzheimer disease (AD). In addition, bioinformatics pipeline techniques were implied for matching genetic variations. Moreover, a novel ML approaches to construct a framework for large scale gene-gene interactions were addressed. Particle swam optimization (PSO) based cancer cytology is another discussed pivotal field. An assembly ML Random forest algorithm was mentioned as it was carried out to classify the features that are responsible for Bacterial vaginosis (BV) in vagina microbiome. Karhunen-Loeve transformation assures features finding from various level of ChIP-seq genome dataset. In the current work, some significant comparisons were conducted based on several ML techniques used for diagnosis medical datasets.

   4. Machine Learning Models for Multidimensional Clinical Data

      Christina Orphanidou, David Wong

      Abstract

      Healthcare monitoring systems in the hospital and at home generate large quantities of rich-phenotype data from a wide array of sources. Typical sources include clinical observations, continuous waveforms, lab results, medical images and text notes. The key clinical challenge is to interpret these in a way that helps to improve the standard of patient care. However, the size and complexity of the data sets, which are often multidimensional and dynamically changing, means that interpretation is extremely difficult, even for expert clinicians. One important set of approaches to this challenge is Machine Learning Systems. These are systems that analyse and interpret data in a way that automatically recognizes underlying patterns and trends. These patterns are useful for predicting future clinical events such as hospital re-admission, and for determining rules within clinical decision support tools. In this chapter we will provide a review of machine learning models currently used for event prediction and decision support in healthcare monitoring. In particular, we highlight how these approaches deal with multi-dimensional data. We then discuss some of the practical problems in implementing Machine Learning Systems. These include: missing or corrupted data, incorporation of heterogeneous and multimodal data, and generalization across patient populations and clinical settings. Finally, we discuss promising future research directions, including the most recent developments in Deep Learning.

   5. Data Quality in Mobile Sensing Datasets for Pervasive Healthcare

      Netzahualcóyotl Hernández, Luis A. Castro, Jesús Favela, Layla Michán, Bert Arnrich

      Abstract

      Mobile sensing is becoming a popular approach for inferring patterns of activity and behavior to determine how they affect health and wellbeing. This data-driven approach has the potential to become a major tool in the field of epidemiology, aimed at determining the causes of disease in populations, as well as motivating behavior change. These sensing technologies are generating large datasets that demand significant processing and data management resources. Studies in mobile sensing for healthcare have motivated the creation of large, complex datasets with information opportunistically gathered from distributed sensors in mobile devices. In this chapter, we discuss some of the architectural challenges regarding data gathering in this distributed data-intensive environment such as the healthcare industry, as well as issues regarding the organization and sharing of the large amounts of data collected. Some of these issues include the heterogeneity of the devices, diversity of sensors used, and the need for data provenance when integrating datasets from diverse studies. We highlight that assessing data quality is of paramount importance for conducting longitudinal studies and building on historical knowledge as new data become available. Finally, we identify future research topics in the growing field of mobile sensing and its application to healthcare and wellbeing. We discuss aspects of data curation, data quality, and data provenance, and we provide suggestions on how these challenges could be addressed in the near future.

5. Internet-of-Things, Fog Computing, and m-Health

   1. Frontmatter

   2. Internet of Things Based E-health Systems: Ideas, Expectations and Concerns

      Mirjana Maksimović, Vladimir Vujović

      Abstract

      Even the interaction between technology and healthcare has a long history, the embracing of e-health is slow because of limited infrastructural arrangements, capacity and political willingness. Internet of Things (IoT) is expected to usher in the biggest and fastest spread of technology in history, therefore together with e-health will completely modify person-to-person, human-to-machine and machine-to-machine (M2M) communications for the benefit of society in general. It is anticipated that the IoT-based e-health solutions will revolutionize the healthcare industry like nothing else before it. The rapid growth of IoT, Cloud computing and Big data, as well as the proliferation and widespread adoption of new technologies and miniature sensing device, have brought forth new opportunities to change the way patients and their healthcare providers manage health conditions, thus improving human health and well-being. The integration of IoT into the healthcare system brings numerous advantages, such as the availability and accessibility, the ability to provide a more “personalized” system, and high-quality cost-effective healthcare delivery. Still, the success of the IoT-based e-health systems will depend on barriers needed to overcome in order to achieve large-scale adoption of e-health applications. A large number of significant technological improvements in both hardware and software components are required to develop consistent, safe, effective, timely, flexible, patient-centered, power-efficient and ubiquitous healthcare systems. However, trust, privacy and security concerns, as well as regulation issues, identification, and semantic interoperability are pivotal in the widespread adoption of IoT and e-health together. Therefore, developing a climate of trust is one of the most important tasks that must be accomplished for successful e-health implementations. This chapter analyzes the ideas and impacts of IoT on the design of new e-health solutions and identifies the majority of challenges that determine successful IoT-based e-health system adoption.

   3. Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

      Harishchandra Dubey, Admir Monteiro, Nicholas Constant, Mohammadreza Abtahi, Debanjan Borthakur, Leslie Mahler, Yan Sun, Qing Yang, Umer Akbar, Kunal Mankodiya

      Abstract

      In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: (1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; (2) The data, when communicated, are vulnerable to security and privacy issues; (3) The communication of the continuously collected data is not only costly but also energy hungry; (4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

   4. Technologies and Practices: Mobile Healthcare Based on Medical Image Cloud and Big Data (in China)

      Jianzhong Hu, Weihong Huang, Yedong Huang

      Abstract

      Modern information technologies such as mobile computing, cloud computing and big data have brought new possibilities for modern healthcare services. In developing countries such as China where the vast majority of healthcare has been delivered in hospitals, mobile healthcare is of great significance to provide easy and quality care for everyone in and out of hospitals. In response to China’s national strategy “Healthy China 2030”, a nationwide hierarchical medical system needs to be established in due course. Featuring ubiquitous access with quality guarantee and consistent user experience on different terminals anytime anywhere, mobile healthcare has been regarded as one of the most important means to support the grand mission of hierarchical medical system. In the process of implementing mobile healthcare, all types of medical data (e.g. patient information, medical records, medical images, health check data, etc.) are to be shared across hospitals boarders. From the computing perspective, mobile medical imaging is regarded as the most challenging issue as medical image processing is the most network and computation resource consuming. Based on systematic requirement analysis, this chapter presents an innovative mobile medical image cloud system. The system enables seamless integration of multiple types of medical data especially medical image data from different vendors globally, and forms the basis for Big Data analysis for smarter healthcare in the future. A real-world case study of cloud teleconsultation using medical image cloud and big data technologies is also presented to prove its technical feasibility and replicability in practice in China.

   5. Large-Scale Innovations and Approaches for Community Healthcare Support in Developing Nations

      Shah Jahan Miah

      Abstract

      Over the past two decades, many large-scale innovations have been designed for the individuals’ information support in improving public healthcare. Studies show rapidly growing interests on cloud computing and telecommunication-based technologies such as mobile-based innovations that are mainly evident in form of improving the social healthcare support systems for community, organisations and individuals. Approaches for various innovations to healthcare support delivery enable people to build on their strengths and to improve the independence and overall wellbeing in the community. The objective of such innovations for community healthcare has been well-established in developed nations, but still emergent to achieve various goals for many developing nations. A lot of application aspects are therefore under-researched to achieve the outcomes such as for encouraging healthy lifestyle choices [4, 8], for individual’s wellness monitoring [31], and in providing general-healthcare information and advice for self-management [21]. This chapter describes issues of the innovative large-scale technological developments for the community healthcare and well-being in context of developing nations, from an angle of service receivers’ perspective. The discussion in the chapter will also capture on various useful large-scale technologies and their effective provisions. In relation to the software-as-service and other forms of cloud technologies as well as the mobile health infrastructure are discussed as they would be useful for the benefit of healthcare service receivers, and through them how individuals can be able to achieve services in the community for enhanced self-management-oriented healthcare.

6. Wearable Computing for Smart Healthcare

   1. Frontmatter

   2. Wearable Computers for Sign Language Recognition

      Jian Wu, Roozbeh Jafari

      Abstract

      A Sign Language Recognition (SLR) system translates signs performed by deaf individuals into text/speech in real time. Low cost sensor modalities, inertial measurement unit (IMU) and surface electromyography (sEMG), are both useful to detect hand/arm gestures. They are capable of capturing signs and are complementary to each other for recognizing signs. In this book chapter, we propose a wearable system for recognizing American Sign Language (ASL) in real-time, fusing information from an inertial sensor and sEMG sensors. The best subset of features from a wide range of well-studied features is selected using an information gain based feature selection approach. Four popular classification algorithms are evaluated for 80 commonly used ASL signs on four subjects. With the selected feature subset and a support vector machine classifier, our system achieves 96.16 and 85.24% average accuracies for intra-subject and intra-subject cross session evaluation respectively. The significance of adding sEMG for American Sign Language recognition is explored and the best channel of sEMG is highlighted.

   3. Real-Time, Personalized Anomaly Detection in Streaming Data for Wearable Healthcare Devices

      Bharadwaj Veeravalli, Chacko John Deepu, DuyHoa Ngo

      Abstract

      Ubiquitous deployment of low cost wearable healthcare devices and proactive monitoring of vital physiological data, are widely seen as a solution for the high costs and risks associated with personal healthcare. The healthcare data generated from these sensors cannot be manually analyzed for anomalies by clinicians due to its scale and therefore automated techniques has to be developed. Present approaches in literature depends on accurate detection of features from the acquired signal which is not always realistic due to noisy nature of the ambulatory physiological data obtained from the sensors. In addition, present anomaly detection approaches require manual training of the system for each patient, due to inherent variations in the morphology of physiological signal for each user. In this chapter, we will first introduce the system architecture for wearable health-care monitoring systems and present discussions on various components involved. Then we discuss on the complexities involved in realizing these methods and highlight key features. We then present our experiences in extracting the ECG segments in real-time and detecting any anomalies in the streams. Particularly, we apply real-time signal processing methods and heuristics to estimate the boundary limits of individual beats from the streaming ECG data. We discuss the importance of designing methods, which are blind to inherent variations among multiple patients and less dependent on the accuracy of the feature extraction. The proposed methods are tested on public database from physionet (QTDB) to validate the quality of results. We highlight and discuss all the significant results and conclude the chapter by proposing some open-ended research questions to be addressed in the near future.

   4. Activity Recognition Based on Pattern Recognition of Myoelectric Signals for Rehabilitation

      Oluwarotimi Williams Samuel, Peng Fang, Shixiong Chen, Yanjuan Geng, Guanglin Li

      Abstract

      Limb-amputation, stroke, trauma, and some other congenital anomalies not only decrease patients’ quality of life but also cause severe psychological burdens to them. Several advanced rehabilitation technologies have been developed to help patients with limb disabilities restore their lost motor functions. As a kind of neural signal, surface electromyogram (sEMG) recorded on limb muscles usually contain rich information associated with limb motions. By decoding the sEMG with pattern recognition techniques, the motion intents can be effectively identified and used for the control of rehabilitation devices. In this chapter, the control of upper-limb prostheses and rehabilitation robots based on the pattern recognition of sEMG signals was detailedly introduced and discussed. In addition, the clinical feasibility of sEMG-based pattern recognition technique towards an improved function restoration for upper-limb amputees and stroke survivors is also described.

   5. Infrequent Non-speech Gestural Activity Recognition Using Smart Jewelry: Challenges and Opportunities for Large-Scale Adaptation

      Mohammad Arif Ul Alam, Nirmalya Roy, Aryya Gangopadhyay, Elizabeth Galik

      Abstract

      Wearable Body Area Network (BAN) based activity recognition is one of the fastest growing research areas in activity recognition and context reasoning. However, wearable physical sensor based Infrequent Non-Speech Gestural Activity (IGA) recognition is not well studied problem because IGAs are not directly observable from BAN sensor devices. Due to the recent proliferation of smart jewelries capable of monitoring locomotive and physiological signals from certain specific human body positions which are currently hitherto impossible to measure by traditional fitness and smart wristwatch devices opens up unprecedented research and development opportunities in anatomical gestural activity recognition. Inspired by this, we propose a new wearable smart earring based framework which is capable of differentiating IGAs in a daily environment with a single integrated accelerometer sensor. The natural gestures associated with the first portion of the human alimentary canal, i.e., human mouth can broadly be categorized in two types; frequent (talking, silence etc.) or infrequent (coughing, deglutition, yawning) gestures. Infrequent Gestural Activities (IGAs) help create an abrupt but distinct change in accelerometer sensor signal streams of an earring pertaining to specific activities. Mining and classifying the abrupt changes in sensor signal streams require high sampling frequency which in turn depletes the limited battery life of any smart ornaments. Extending the battery life of smartened designer jewelry requires probing those devices less which in turn prohibits of achieving high precision and recall for non-frequent gestural activity discovery and recognition. In this book chapter, we propose a novel data segmentation technique that harnesses the power of change-point detection algorithm to detect and quantify any abrupt changes in sensor data streams of smart earrings. This helps to distinguish between frequent and infrequent gestural acclivities at a high precision with a low sampling frequency, energy, and computational overhead. Experimental evaluation on one real-time and two publicly available benchmark datasets attests the scalability and adaptation of our techniques for both IGAs and postural activities in large-scale participatory sensing health applications.

   6. Smartphone Based Real-Time Health Monitoring and Intervention

      Daniel Aranki, Gregorij Kurillo, Ruzena Bajcsy

      Abstract

      Smartphones are often dubbed as “a doctor in your pocket” as they have in recent years become one of the most notable platforms for health management and monitoring. In this chapter we discuss the potentials for real-time health monitoring of chronic health conditions and data-driven intervention that aim to improve patient care at a lower cost. We outline several challenges that developers, patients, and providers face with respect to this new technology. We then review several commercial platforms for health monitoring and discuss their pros and cons. Furthermore, we present Berkeley Telemonitoring Framework, a recently developed Andorid-based open source solution for development of health-monitoring applications with security and privacy in mind. In particular, our framework offers an easy-to-use API for building client apps, deploying data-hosting servers, fault-tolerant data retrieval and storage, access to event-based Bluetooth and BLE stacks with standards for personal health devices, access to phone sensors, implementation of several vital signs estimators, gait analysis, etc. We demonstrate the use of the framework on an example fitness application MarathonCoach. We further discuss several challenges facing real-time telemonitoring. In particular, we focus on privacy and propose a novel information-theoretic framework called Private Disclosure of Information (PDI). The PDI framework formalizes a scheme for encoding the collected health data in a manner that minimizes the ability of an adversary from gaining knowledge about the patient’s diagnosis (or other information private by implication) through statistical inference, while allowing the authorized provider to use this information with no loss in utility.

   7. Exploiting Physiological Sensors and Biosignal Processing to Enhance Monitoring Care in Mental Health

      Gaetano Valenza, Enzo Pasquale Scilingo

      Abstract

      In this chapter, we describe how it is possible to exploit physiological sensors and related signal processing methods to enhance monitoring care mental health. Specifically, focusing on wearable sensors for Autonomic Nervous System (ANS) dynamics, we report on recent progresses in monitoring mood swings associated with bipolar disorder through the so-called PSYCHE system. Current clinical practice in diagnosing patients affected by this psychiatric disorder, in fact, is based only on verbal interviews and scores from specific questionnaires. Furthermore, no reliable and objective psycho-physiological markers are currently taken into account. We particularly describe a pervasive, wearable, and personalized system based on a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire electrocardiogram, respirogram, and body posture information. In order to identify a pattern of objective physiological parameters to support the diagnosis, we describe ad-hoc methodologies of advanced biosignal processing able to effectively recognize four possible clinical mood states in bipolar patients (i.e., depression, mixed state, hypomania, and euthymia) who underwent long-term (up to 24 h) monitoring. Mood assessment is here intended as an intra-subject evaluation in which the patient’s states are modeled as a stochastic process with time dependency, i.e., in the time domain, each mood state refers to the previous one(s). Experimental results are reported in terms of statistical analysis, as well as confusion matrices from automatic mood state recognition, and demonstrate that wearable and comfortable ANS monitoring could be a viable solution to enhance monitoring care in mental health. We conclude the chapter describing a methodology predicting mood changes in bipolar disorder using heartbeat nonlinear dynamics exclusively.

7. Resource Allocation, Quality of Service (QoS), and Context-Awareness in Smart Healthcare

   1. Frontmatter

   2. Resource Allocation in Body Area Networks for Energy Harvesting Healthcare Monitoring

      Shiyang Leng, Aylin Yener

      Abstract

      Health monitoring body area networks (BANs) have the potential to create a paradigm shift in providing personal healthcare “Johny, Anpalagan (IEEE Potentials 33(2):21-25, 2014) [1].” A BAN consists of multiple wireless body sensors attached to or implanted in the human body to continuously monitor the patient’s vital signs.

   3. Medical-QoS Based Telemedicine Service Selection Using Analytic Hierarchy Process

      Ali Hassan Sodhro, Faisal K. Shaikh, Sandeep Pirbhulal, Mir Muhammad Lodro, Madad Ali Shah

      Abstract

      An emerging breakthrough paradigm shift in health industry and wearable devices, large scale and distributed mobile cloud computing technologies have led to new opportunities for medical healthcare systems. Telemedicine service selection and management of Medical-Quality of Service (m-QoS) in large-scale and distributed medical health system (e.g. medical data centers, hospitals, medical servers and medical clouds, etc.) is a key challenge for both industry and academia. The aim of this chapter is to improve and manage m-QoS by prioritizing Telemedicine service by using decisive and intelligent tool called Analytic Hierarchy Process (AHP). This service will be provided on urgency basis from the pool of medical services with the help of AHP. In this connection, four telemedicine services are considered i.e. Tele-surgery, Tele-Consultation, Tele-Education and Tele-Monitoring. In this research, three m-QoS parameters are considered i.e. throughput, jitter and delay. These services are evaluated by potential doctors and patients. We propose an AHP based decision making algorithm for selecting urgent and important service for the fast and cost-effective treatment of the emergency patients at the remote location in the hospital, because AHP is the significantly fast decision making technique used to assess, select and manage the emergency services at various priority levels in large scale and distributed medical health systems. The comprehensive purpose is indicated in the first level of the strategy. The decisive entities are presented in the intermediate level and the target-based alternatives are located at the lowest level. MATLAB is used for experimental results to measure and evaluate goal, decision making parameters and options from both qualitative and quantitative aspect. The proposed AHP algorithm is simulated for three decision parameters and four different Telemedicine services in which highest priority is given to decision parameter, throughput and Telemedicine service, Tele-Surgery for large scale and distributed medical health systems.

   4. Development and Application of a Generic Methodology for the Construction of a Telemonitoring System

      Amir Hajjam El Hassani, Amine Ahmed Benyahia, Emmanuel Andres, Samy Talha, Mohamed Hajjam El Hassani, Vincent Hilaire

      Abstract

      Telemonitoring systems are nowadays being extensively developed and utilized, due to the fact that the worldwide elderly population is increasing. In fact, the use of a telemonitoring system alleviate the problem of health costs by providing a reliable way of alerting the healthcare personnel. The design of a telemonitoring system is a real challenge. In this context, the architecture of a telemonitoring system must be generic and flexible and its knowledge must be well defined so it can be shared between actors of the system. In this paper, we present a methodology for the design of a telemonitoring system. This methodology is based on the use of multi-agent system, ontologies and expert systems. The proposed approach relies on an existing multi-agent methodology known as ASPECS. The latter is adapted to construct a telemonitoring system by adding several activities that introduce ontologies and expert systems. This methodology is applied to E-care, a platform designed for a large scale computing. E-care is a telemonitoring platform for patients suffering from heart failure. As part of this platform, several experiments were conducted to validate this methodology at Strasbourg University Hospital (Strasbourg, France). Preliminary results show that this platform is able to assist health care professionals. E-care processes data sent from the sensors and generates automatically alerts in order to detect early risk situations of heart failure.

   5. Ontology-Based Contextual Information Gathering Tool for Collecting Patients Data Before, During and After a Digestive Surgery

      Lamine Benmimoune, Amir Hajjam, Parisa Ghodous, Emmanuel Andres, Mohamed Hajjam

      Abstract

      In the health domain, computer-based questionnaires are beneficial since they permit the collection of important elements regarding patients health status. These elements are generally used as input data for many medical systems such as health monitoring systems. The aim of this paper is to describe our contextual Information Gathering Tool (IGT). This tool permits to gather data by providing contextual questionnaires based on the question/answer mechanism and distributed architecture. Our proposed IGT is based on the use of an interrogation engine and ontologies. The engine provides contextual questionnaire as function of the user context and adapts questions depending on the users answer. The use of ontologies permits to model questionnaires and interrogations history. Moreover, ontologies are used to control the creation of questionnaires by offering meanings to the asked questions and then to the collected data. The proposed IGT is used in a clinical setting as a part of the E-care medical monitoring platform. It is applied to the rehabilitation process after a digestive surgery. The tool gathers contextual data relative to the patients hospitalization phase (i.e. before, during and after the surgery). The collected data are then represented graphically for statistical purposes and analyzed by the medical platform to make decisions regarding the patients health status (i.e. warning medical staff if dangerous situations are detected, generating health status indicators, providing useful therapeutic recommendations, etc.).