Clinical decision-support functions of telemedicine systems use patient's monitored clinical data to support treatment of outpatients. However, the quality of monitored clinical data may vary due to performance variations of technological resources inside a deployed telemedicine system. This paper discusses models to compute quality of clinical data affected by quality of service provided by technological resources along the data processing and delivery chain between the point of monitoring and point of decision. We discuss prospective effects of quality of clinical data degradation on outpatient treatment with medical practitioners, and implement these effects in the clinical decision-making process during design time. Consequently, the designed telemedicine system is technological context and quality-aware and preserves patient's safety and treatment efficacy.

We present a conceptual framework for modelling clinical guidelines as networks of concurrent processes. This enables the guide- line to be partitioned and distributed at run-time across a knowledge-based telemedicine system, which is distributed by definition but whose exact physical configuration can only be determined after design-time by considering, amongst other factors, the indi- vidual patient's needs. The framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype that executes the guideline on a smartphone. The framework is shown to support the full development trajectory of a decision support system, including analysis, design and implementation.

Ubiquity of Information and Communication Technology enables innovative telemedicine treatment applications for disease management of ambulant patients. Development of new treatment applications must comply with medical protocols and `way of working' to obtain safety and efficacy evidence before acceptance and use by medical practitioners. Usually, medical researchers design new treatment applications and engineers elicit application requirements in collaboration with these researchers to bridge the knowledge and `way of working' gaps between them. This paper presents an elicitation method for new telemedicine applications in a collaborative setting of time-constraint medical practitioners and requirements engineers if the medical researcher is absent. Engineers compensate this lack of resources through cross-disciplinary studies and use of pathophysiological models in the absence of medical evidence. The paper discusses the application of a mixed elicitation method presented in earlier work in the addressed setting. The method applies a scenario based user needs analysis augmented by domain activity and user-system interaction analysis. The elicitation is conducted in a separation of concerns fashion combined with collaboration handshake protocols to align domain activities and user-system interactions. Later phase elicitation of user-system interaction requirements may apply known methods and is not addressed.

This paper presents the early phase requirements elicitation for a work-related neck-shoulder pain teletreatment trial and the assessment of those requirements in respect of their importance to the trial and the feasibility of the needed software adaptations of the telemedicine system within the constraints of the project organizing the trial. The elicitation adopts specific solutions and constraints that are typical for working practices in medicine. In particular, the contribution of this paper is its holistic approach in addressing the intertwining of the requirements elicitation and the trial design. Trial design defines the trial settings and the treatment protocols in a standardized way such that treatment efficacy can be evaluated based on evidence. A cyclic approach which applies a set of elicitation techniques and informal specification styles (e.g. tables and mock-ups) is adopted in order to converge on a set of requirements understood and agreed upon also by the trial designers, who are medical professionals, often unaware of formal methods, and who co-shape the telemedicine system.

Mobile Patient Monitoring System (MPMS) is positioned to provide high quality healthcare services in the near future. The gap between its application demands and resource supplies, however, still remains and may hinder this process. Dynamic context-aware adaptation mechanisms are required in order to meet the stringent requirements on such mission critical applications. The fundamental model underlying an MPMS includes a set of biosignal data processing tasks distributed across a set of networked devices. In our earlier work, we designed and validated a task distribution framework to support dynamic system reconfiguration of MPMS by means of task redistribution. This paper focuses on its decision-making component that can calculate the optimal task assignment by taking into account the reconfiguration costs. This paper has three major contributions. Firstly, we study a context-aware scenario and derive the design requirements for a task assignment algorithm in MPMS. Secondly, using a graph-based system model, we proposed an A*-based task assignment algorithm that minimizes the system end-to-end delay while guaranteeing required system battery lifetime and availability. We introduce a set of node expansion rules and a pre-processing procedure to calculate the heuristic function (h(n)). Thirdly, we evaluate the algorithm performance with experiments and compare this A*-based algorithm with other heuristic approaches, e.g. greedy and bounded A*.

In recent years, there has been much research on ontologies for geographic information (GI) services. But to date, focus has been on semantics of data and operations. Much less attention has been given to semantics of quality of GI services. In addressing this gap, this paper proposes an ontology framework for quality of GI services. The framework has an upper ontology, two primary domain ontologies and potentially many application ontologies. The ontologies provide concepts for unambiguously defining and reasoning about quality of GI services. We see the main strength of the framework as being its holistic view of quality; it integrates data quality and quality of service (QoS) which hitherto have been defined separately. Besides, the framework builds on established data quality and QoS concepts which guarantees its relevance and acceptance.

We are investigating the use of body area networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can view a multimedia display which includes graphical and numerical representation of patientspsila biosignals. Addition of feedback-control enables teletreatment services; teletreatment can be delivered to the patient via multiple modalities including tactile, text, auditory and visual. We describe the health BAN and a generic mobile health service platform and two context aware applications. The epilepsy application illustrates processing and interpretation of multi-source, multimedia BAN data. The chronic pain application illustrates multi-modal feedback and treatment, with patients able to view their own biosignals on their handheld device.

With the advent of ubiquitous computing, a user is surrounded by a variety of devices including tiny sensor nodes, handheld mobile devices and powerful computers as well as diverse communication networks. In this networked society, the role of a human being is evolving from the data consumer to the data producer. In these changing circumstances, pipelined processing finds applications where the data obtained from the human producer needs to be processed and interpreted in real-time. For example, in an M- Health system, the vital signs acquired from the patient are processed in the pipelined fashion. This paper proposes a genetic algorithm (GA) based approach for the optimal assignment of pipelined processing tasks onto a chain of networked devices that minimizes total end-to-end processing delay considering knowledge about the communication and computation resources as the context information. Although some existing graph-based algorithms can solve this problem in polynomial time, we expect that GA can take less computational time and requires less memory while providing a reasonably good assignment. We compare the performance of GA approach with the graph-based approaches. It is observed that when the number of devices and the number of processing tasks are large, the GA approach performs better in terms of the satisfactory quality of the obtained sub-optimal solution considering the advantage of reduced computational time.

In this paper, we study the problem of an optimal assignment of a tree-structured context reasoning procedure onto the computation resources in a host-satellites configuration. The objective function to be minimized is the end-to-end processing delay, which is a crucial factor in a number of context-aware applications, e.g. mobile healthcare applications. The presented solution is a modification of an earlier method proposed by Bokhari, in which the optimal assignment problem to minimize the bottleneck processing time is transformed into a path-searching problem in a doubly weighted graph. Due to the incompatible requirements raised in our study, e.g. a-prior known location of the sensors, we propose a colouring scheme and a new search algorithm in this paper to obtain the optimal assignment in order to satisfy our objective.

This paper addresses how quality of context (QoC) can be used to optimize end-to-end mobile healthcare (m-health) data delivery services in the presence of alternative delivery paths, which is quite common in a pervasive computing and communication environment. We propose min-max-plus based algebraic QoC models for computing the quality of delivered data impeded by the QoS of the resources along the alternative delivery paths. The constructed algebraic structures in those models directly relate to the resource configurations represented as directed graphs. The properties of the applied algebras correspond to the properties of the operations of the addressed QoS dimensions. To rank all the possible resource configurations and therewith select from those the most optimal one(s) we introduce a workflow management metric based on the quality dimensions like freshness and availability. We focus on the pre-establishment phase of m-health data delivery services; dynamic QoC issues existing during service execution are not considered

This paper reports on applying the ODP enterprise viewpoint in the domain of tele-education. The work is conducted as part of a research activity that aims at designing a tele-education system to support planning, execution and evaluation of dynamic distributed educational processes. We explore the ODP enterprise viewpoint as a basis for communication and co-operation between educational scientists and ODP systems designers involved in the design process. Our application of the enterprise viewpoint involves four main steps. First, an educational language is proposed to describe educational processes in generic terms. Second, a set of appropriate enterprise language concepts is selected. Third, a relationship is established between the educational language and enterprise language concepts. Fourth, an educational process is modelled in terms of an entity-oriented model and a behaviour-oriented model. It is hoped that the experience gained in this exercise will provide useful feedback to both the educational and ODP communities.

We consider the modelling of a covariance function δ(t) + k(t) where t is continuous time. For instance, it is of interest to have a method for computing a continuous model of the oral cavity. To achieve this one uses a sampling of the speech signal which is high compared to the Nyquist frequency and one hopes to be able to distill from it a time continuous reflection function. The problem here, however, is one of numerical stability. By increasing the sampling and using classical methods one will lose control on accuracy. In order to stabilize the procedure we have investigated how the celebrated Schur method [1],[2],[3] may be extended to the time-continuous case. It turns out that a novel equation arises which expresses the causality of the modeling filter and which, when used on our problem, achieves a high degree of numerical stability. Incidentally we show also how the method is related to classical inverse scattering problems for lossless transmission lines. In fig. 1-4 we show a number of covariance testfunctions, their corresponding continuous-time reflection functions and their errors. As a conclusion we may state that we arrive at an optimal behaviour of the algorithm, even in the presence of noise, as is illustrated in fig. 5. In conclusion we discuss the relation of our technique with methods presented in estimation theory [4],[5], in electromagnetism [6] and in embedding theory [7].