MobiHealth: Ambulant Patient Monitoring Over Next Generation Public Wireless Networks

The wide availability of high bandwidth public wireless networks as well as the miniaturisation of medical sensors and network access hardware allows the development of advanced ambulant patient monitoring systems. The MobiHealth project developed a complete system and service that allows the continuous monitoring of vital signals and their transmission to the health care institutes in real time using GPRS and UMTS networks. The MobiHealth system is based on the concept of a Body Area Network (BAN) allowing high personalization of the monitored signals and thus adaptation to different classes of patients. The system and service has been trialed in four European countries and for different patient cases. First results confirm the usefulness of the system and the advantages it offers to patients and medical personnel

Mobile Patient Monitoring: The Mobihealth System

The forthcoming wide availability of high bandwidth public wireless networks will give rise to new mobile healthcare services. To this end, the MobiHealth project has developed and trialed a highly customisable vital signs monitoring system based on a body area network (BAN) and a mobile-health (m-health) service platform utilising next generation public wireless networks. The developed system allows the incorporation of diverse medical sensors via wireless connections, and the live transmission of the measured vital signs over public wireless networks to healthcare providers. Nine trials with different healthcare scenarios and patient groups in four different European countries have been conducted. These have been performed to test the service and the network infrastructure including its suitability for mobile healthcare applications. Preliminary results have documented the feasibility of using the system, but also demonstrated logistical problems with use of the BANs and the infrastructure for transmitting mobile healthcare data

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

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e. m-health) services allow healthcare professionals to monitor a mobile patient�s vital signs and provide feedback to this patient anywhere and any time. Due to the nature of current supporting mobile services platforms, mhealth 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 contextual information in an mhealth services platform improves the delivered QoS. We give a first attempt to merge contextual information with a QoS-aware mobile services platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

QoS-predictions service: infrastructural support for proactive QoS- and context-aware mobile services

Today’s mobile data applications aspire to deliver services to a user anywhere - anytime while fulfilling his Quality of Service (QoS) requirements. However, the success of the service delivery heavily relies on the QoS offered by the underlying networks. As the services operate in a heterogeneous networking environment, we argue that the generic information about the networks’ offered-QoS may enable an anyhow mobile service delivery based on an intelligent (proactive) selection of ‘any’ network available in the user’s context (location and time). Towards this direction, we develop a QoS-predictions service provider, which includes functionality for the acquisition of generic offered-QoS information and which, via a multidimensional processing and history-based reasoning, will provide predictions of the expected offered-QoS in a reliable and timely manner. We acquire the generic QoS-information from distributed mobile services’ components quantitatively (actively and passively) measuring the applicationlevel QoS, while the reasoning is based on statistical data mining and pattern recognition techniques

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

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e. m-health) services allow healthcare professionals to monitor a mobile patient’s vital signs and provide feedback to this patient anywhere and any time. Due to the nature of current supporting mobile services platforms, mhealth 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 contextual information in an mhealth services platform improves the delivered QoS. We give a first attempt to merge contextual information with a QoS-aware mobile services platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

Body Area Networks for Ambulant Patient Monitoring Over Next Generation Public Wireless Networks

The forthcoming wide availability of high bandwidth public wireless networks combined with the evolution of performant body area networks will give rise to new mobile health care services. The MobiHealth , , project has developed and trialed a customisable vital signals’ monitoring system based on a Body Area Network (BAN) and an m-health service platform utilizing UMTS and GPRS networks. The developed system allows the incorporation of diverse medical sensors via wireless connections, and the live transmission of the measured vital signals over public wireless networks to healthcare providers. \ud Nine trials with different healthcare cases and patient groups in four different European countries have been conducted to test and verify the system, the service and the network infrastructure for its suitability and the restrictions it imposes to mobile health care applications

Goodput Analysis of 3G Wireless Networks Supporting m-health Services

The introduction of third generation (3G) public wireless network infrastructures, such as the Universal Mobile Telecommunications System (UMTS), enable the development of innovative mobile services. For example, deploying m-health services which embed tele-monitoring and tele-treatment services become feasible with the role-out of 3G networks. These services allow healthcare professionals to monitor a mobile patient's vital signs and provide feedback to this patient anywhere and any time. The performance of m-health services perceived by endusers depends on the serviceableness of 3G networks to support these services. Hence, the performance of 3G networks is a critical factor for successful development of m-health services. In this paper, we present a methodology for measurements-based performance assessment of 3G networks that aim to support m-health services. This methodology has been applied to evaluate end-user perceived service performance, in relation to the performance (i.e. serviceableness) of a 3G network. In addition, we analyse the measurements with the purpose to improve the end-to-end delay characteristics of the telemonitoring service as well as optimize the (derived) goodput behaviour of this 3G network. Our results show that the goodput behaviour is asymmetric and depends on a bearer assignment policy of the network. Based on our results we provide guidelines for the design of application protocols for m-health services and how these protocols deal with the changing performance behaviour of 3G networks

Accuracy evaluation of application-level performance measurements

In many cases, application-level measurements can be the only way for an application to evaluate and adapt to the performance offered by the underlying networks. Applications perceive heterogeneous networking environments spanning over multiple administrative domains as ��?black boxes��? being inaccessible for lower-level measurement instrumentation. However, application-level measurements can be inaccurate and differ significantly from the lower-level ones, amongst others due to the influence of the protocol stacks. In this paper we quantify and discuss such differences using the Distributed Passive Measurement Infrastructure (DPMI), with Measurement Points (MPs) instrumented with DAG 3.5E cards for the reference link-level measurements. We shed light on various impacts on timestamp accuracy of application-level measurements. Moreover, we quantify the accuracy of generating traffic with constant inter-packettimes (IPTs). The latter is essential for an accurate emulation of application-level streaming traffic and thus for obtaining realistic end-to-end performance measurements

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

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