Jarosław Drapała

Supporting content, context and user awareness in future internet applications

We show how the applications utilizing a Future Internet architecture can benefit from its features like quality of service (QoS) provisioning and resources reservation.We demonstrate, how proposed applications address content, context and user awareness basing on the underlying Next Generation Network (NGN) infrastructure and how it can be used to host service-based applications.

ADAPTIVE DECISION SUPPORT SYSTEM FOR AUTOMATIC PHYSICAL EFFORT PLAN GENERATION: DATA-DRIVEN APPROACH

Mathematical models delivered using both expert knowledge and experimental data improve understanding of dynamic properties of the system under consideration. This is useful for different purposes, such as prediction, diagnosis, decision making, and system control. A data-driven approach has been found to be particularly useful in designing adaptive decision support systems. We demonstrate the usefulness of data-driven models in a custom application designed for sport training management. We have developed a system that makes use of expert knowledge together with measurement data (heart rate, electromyography, and acceleration) as well as environmental (Global Positioning System) in order to generate an optimal training plan. The system performs such tasks as modeling of the athletes cardiovascular system, estimation of the athletes parameters, and adaptation of the model to the athlete.

System analysis techniques in ehealth systems: a case study

In the paper problem of planning training protocol with taking into account limitations on the training intensity due to the health problems of the exerciser is considered. In the first part of the work short introduction to existing solutions in the area of eHealth applications is given. Next, architecture of the eHealth system to support exerciser training is discussed. The main functionalities of proposed system are pointed out and challenges are highlighted. The concept of context-awareness and personalization is stressed. At the end the problem of model based optimisation of the training protocol is formulated.

Impairments of working memory in schizophrenia and bipolar disorder: the effect of history of psychotic symptoms and different aspects of cognitive task demands.

Comparisons of cognitive impairments between schizophrenia (SZ) and bipolar disorder (BPD) have produced mixed results. We applied different working memory (WM) measures (Digit Span Forward and Backward, Short-delay and Long-delay CPT-AX, N-back) to patients with SZ (n = 23), psychotic BPD (n = 19) and non-psychotic BPD (n = 24), as well as to healthy controls (HC) (n = 18) in order to compare the level of WM impairments across the groups. With respect to the less demanding WM measures (Digit Span Forward and Backward, Short-delay CPT-AX), there were no between group differences in cognitive performance; however, with respect to the more demanding WM measures (Long-delay CPT-AX, N-back), we observed that the groups with psychosis (SZ, psychotic BPD) did not differ from one another, but performed poorer than the group without a history of psychosis (non-psychotic BPD). A history of psychotic symptoms may influence cognitive performance with respect to WM delay and load effects as measured by Long-delay CPT-AX and N-back tests, respectively. We observed a positive correlation of WM performance with antipsychotic treatment and a negative correlation with depressive symptoms in BPD and with negative symptoms in SZ subgroup. Our study suggests that WM dysfunctions are more closely related to a history of psychosis than to the diagnostic categories of SZ and BPD described by psychiatric classification systems.

Money matters: does the reimbursement policy for second-generation antipsychotics influence the number of recorded schizophrenia patients and the burden of stigmatization?

PurposeIn Poland, non-compliance with the reimbursement policy for second-generation antipsychotics (SGA) manifested in prescribing SGA for patients with psychotic disorders other than schizophrenia may result in serious financial penalties. In this study, we aimed at investigating whether the implementation of the reimbursement policy for SGA contributed to increasing the number of patients with a diagnosis of schizophrenia relatively to the number of patients with a diagnosis of other psychotic disorders in outpatient clinics.MethodsWe analyzed data from Yearbooks of Mental Health that were published by the Institute of Psychiatry and Neurology, Warsaw, Poland in the years 1989–2009 registering the number of patients treated for various mental disorders in public facilities in Poland. Temporal trend analysis of the annual number of patients with a diagnosis of psychotic disorders, who were treated at outpatient clinics, was performed.ResultsWe found a statistically significant increase in the total number of recorded schizophrenia patients treated at outpatient clinics, as well as in the number of patients treated for the first time at outpatient clinics for schizophrenia. These changes overlap with the implementation of the reimbursement policy for SGA.ConclusionOur results suggest that the restricted reimbursement policy for SGA altered the diagnosing process in Poland. It seems that these alterations may have serious social consequences. Given that a diagnosis of schizophrenia is more stigmatizing than a diagnosis of other psychotic disorders, it might be assumed that schizophrenia over-diagnosing, possibly due to reimbursement reasons, add to the enormous burden associated with stigmatization.

Wearable smart system for physical activity support

Wearable smart systems that measure and process a human vital sings have become important tool in eHealth, rehabilitation and recreation. This research was about to develop a system that aims to support recreational physical activity by delivering communication and computational services. The system architecture facilitates implementation of advanced data processing methods in the form of computational services and easily include additional services according to the user needs. An eHealth application was developed to demonstrate the system capabilities. The application makes use of expert’s knowledge together with measurement data in order to generate the optimal training plan. The system performs such tasks as: modelling of the sportsmen’s cardiovascular system, estimation of the sportsmen’s parameters, adaptation of the model to the sportsmen.

Development of Intelligent eHealth Systems in the Future Internet Architecture

One of the most important directions in the development of eHealth systems is their personalization. Body area networks with wireless sensors and portable access devices allow one to design applications for supporting people in their daily activities. On the other hand, they require substantial data analysis and processing which are inherent features of the medical domain. We describe few service-based applications utilizing a Future Internet architecture which supports QoS guarantees for the communication between their components and also show how they can benefit from its features such as quality of service (QoS) provisioning and resources reservation. We demonstrate how proposed applications address content, context and user awareness based on the underlying Next Generation Network (NGN) infrastructure and give examples of decision-making tasks performed by applications.

Application of Nonlinear State Estimation Methods for Sport Training Support

Typical understanding of healthcare concerns treatment, diagnosis and monitoring of diseases. But healthcare also includes well-being, healthy lifestyle, and maintaining good body condition. One of the most important factor in this respect is physical activity. Modern techniques of data acquisition and data processing enable development of advanced systems for physical activity support with use of measurement data. The need for reliable estimation routines stems from the fact, that many widely available for bulk customers measurements devices are not reliable and measured signals are contaminated by the noise. One of the most important variables for physical activity monitoring is the velocity of a moving object e.g. velocity of selected parts of a body such as elbows. Apart from intensive use of system identification, optimization and control techniques for physical training support, we applied Kalman filtering technique in order to estimate speed of moving part of a body.

Multidimensional data visualization applied for user's questionnaire data quality assessment

Questionnaire is a method of evaluating the quality of information system or the quality of its interface and provided services. Its main purpose is to gain knowledge about users themselves and how they work with the system. To draw valuable conclusions from the questionnaire, methods of statistical data analysis are employed. Processing of questionnaire data is important for SOA systems in particular because it helps to improve efficiency of its services utilization. In this paper multi-dimensional data visualization technique, based on the new algorithms, is applied to questionnaire questions selection.

Global and Local Approach to Complex Systems Modeling Using Dynamic Neural Networks--- Analogy with Multiagent Systems

In this paper, the global and local approach to dynamic input-output complex systems modeling is presented. Local and global modeling problems are formulated. Dynamic neural networks learning algorithm for global modeling of complex system has been constructed. Analogy between models of input-output complex systems and multiagent systems is discussed.