101 research outputs found

    MobiHealth-Innovative 2.5/3G mobile services and applications for health care

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    MobiHealth aims at introducing new mobile value added services in the area of healthcare, based on 2.5 (GPRS) and 3G (UMTS) technologies, thus promoting the use and deployment of GPRS and UMTS. This will be achieved by the integration of sensors and actuators to a Wireless Body Area Network (BAN). These sensors and actuators will continuously measure and transmit vital constants along with audio and video to health service providers and brokers, improving on one side the life of patients and allowing on the other side the introduction of new value-added services in the areas of disease prevention and diagnostic, remote assistance, para-health services, physical state monitoring (sports) and even clinical research. Furthermore, the MobiHealth BAN system will support the fast and reliable application of remote assistance in case of accidents by allowing the paramedics to send reliable vital constants data as well as audio and video directly from the accident site

    Context-aware QoS provisioning for an M-health service platform

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    Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient's vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current supporting mobile service platforms, m-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). In this paper, we argue that the use of context information in an m-health service platform improves the delivered QoS. We give a first attempt to merge context information with a QoS-aware mobile service platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

    Body Area Networks for health

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    Mobile Health Care over 3G Networks: the MobiHealth Pilot System and Service

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    Health care is one of the most prominent areas for the application of wireless technologies. New services and applications are today under research and development targeting different areas of health care, from high risk and chronic patients’ remote monitoring to mobility tools for the medical personnel. In this direction the MobiHealth project developed and trailed a system and a service that is using UMTS for the continuous monitoring and transmission of vital signals, like Pulse Oximeter sensor , temperature, Marker, Respiratory band, motion/activity detector etc., to the hospital. The system, based on the concept of the Body Area Network, is highly customisable, allowing sensors to be seamlessly connected and transmit the monitored vital signal measurements. The system and service was trialed in 4 European countries and it is presently under market validation

    An application of augmented MDA for the extended healthcare enterprise

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    Mobile health systems extend the enterprise computing system of the healthcare provider by bringing services to the patient any time and anywhere. We propose a methodology for the development of such extended enterprise computing systems which applies a model-driven design and development approach augmented with formal validation and verification to address quality and correctness and to support model transformation. At the University of Twente we develop context aware m-health systems based on Body Area Networks (BANs). A set of deployed BANs are supported by a server. We refer to this distributed system as a BAN System. Development of such distributed m-health systems requires a sound software engineering approach and this is what we target with the proposed methodology. The methodology is illustrated with reference to modelling activities targeted at real implementations. BAN implementations are being trialled in a number of clinical settings including epilepsy management and management of chronic pain

    Guideline-based decision support for the mobile patient incorporating data streams from a body sensor network

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    We present a mobile decision support system (mDSS) which helps patients adhere to best clinical practice by providing pervasive and evidence-based health guidance via their smartphones. Similar to some existing clinical DSSs, the mDSS is designed to execute clinical guidelines, but it operates on streaming data from, e.g., body sensor networks instead of persistent data from clinical databases. Therefore, we adapt the typical guideline-based architecture by basing the mDSS design on existing data stream management systems (DSMSs); during operation, the mDSS instantiates from the guideline knowledge a network of concurrent streaming processes, avoiding the resource implications of traditional database approaches for processing patient data which may arrive at high frequencies via multiple channels. However, unlike typical DSMSs, we distinguish four types of streaming processes to reflect the full disease management process: Monitoring, Analysis, Decision and Effectuation. A prototype of the mDSS has been developed and demonstrated on an Android smartphone

    Requirements for a Nutrition Education Demonstrator

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    [Context and Motivation] Development of innovative ICT-based applications is a complex process involving collaboration of all relevant disciplines. This complexity arises due to differences in terminology, knowledge and often also the ways of working between developers in the disciplines involved. [Question/problem] Advances in each discipline bring a rich design environment of theories, models, methods and techniques. Making a selection from these makes the development of distributed applications very challenging, often requiring a holistic approach to address the needs of the disciplines involved. This paper describes early stage requirements acquisition of a mobile nutrition education demonstrator which supports overweight persons in adopting healthier dietary behaviour. [Principal idea/results] We present a novel way to combine and use known requirements acquisition methods involving a two stage user needs analysis based on scenarios which apply a theory-based model of behavioural change and are onstructed in two phases. The first phase scenarios specify an indicative description reflecting the use of the transtheoretical model of behavioural change. In the second phase, a handshake protocol adds elements of optative system-oriented descriptions to the scenarios such that the intended system can support the indicative description. [Contribution] The holistic and phased approach separates design concerns to which each of the disciplines contributes with their own expertise and domain principles. It preserves the applied domain principles in the design and it bridges gaps in terminology, knowledge and ways of working

    Experience with Using the Sensewear BMS Sensor System in the Context of a Health and Wellbeing Application

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    An assessment of a sensor designed for monitoring energy expenditure, activity, and sleep was conducted in the context of a research project which develops a weight management application. The overall goal of this project is to affect sustainable behavioural change with respect to diet and exercise in order to improve health and wellbeing. This paper reports results of a pretrial in which three volunteers wore the sensor for a total of 11 days. The aim was to gain experience with the sensor and determine if it would be suitable for incorporation into the ICT system developed by the project to be trialled later on a larger population. In this paper we focus mainly on activity monitoring and user experience. Data and results including visualizations and reports are presented and discussed. User experience proved positive in most respects. Exercise levels and sleep patterns correspond to user logs relating to exercise sessions and sleep patterns. Issues raised relate to accuracy, one source of possible interference, the desirability of enhancing the system with real-time data transmission, and analysis to enable real-time feedback. It is argued that automatic activity classification is needed to properly analyse and interpret physical activity data captured by accelerometry
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