Feasibility study of a sensor-based autonomous load control exercise training system for COPD patients.

Decision support systems (DSSs) which are able to automatically supervise and control physical exercise training of patients affected by chronic obstructive pulmonary disease (COPD) are regarded as a novel method to promote rehabilitation. The objective of our research work for this paper was to evaluate the feasibility of a rule-based DSS for autonomous bicycle ergometer training of COPD patients. Load control is based on real-time analysis of sensor parameters oxygen saturation and heart rate. Ten COPD patients have participated in a study, performing altogether 18 training sessions. On average, 7.4 rules were fired in each training session. Four sessions had to be stopped for different reasons. The average ergometer training load ranged between 31 and 47 W. The average percentage of heart rate in or lower than the intended target zone was 45.9 and 41.6%, respectively. The average patient-perceived Borg value was 12.6±2.4. Patients reported a high satisfaction for the automatically controlled training. With the help of the DSS, patients may change their training place from a rehabilitation center to their own homes. More studies are needed to assess long-term clinical and motivational effects of the DSS in home environment.

A methodological framework for the analysis of highly intensive, multimodal and heterogeneous data in the context of health-enabling technologies and ambient-assisted living.

BACKGROUND: When applying health-enabling technologies (HET), researchers are faced with analyzing highly intensive, multimodal and heterogeneous data sets. Experience has shown that there is a lack of understanding concerning the relationship of analysis methods suitable for such data sets and their appropriate application. OBJECTIVES: The objective of this paper is to describe the present situation when analyzing data of HET and the main problems in this context, to present a nomenclature suitable for analysis methods in the context of HET, to present an example dealing with geriatric diseases that highlights the problems and the urgent need for results and to explain some steps for future work. METHODS: Nomenclatures as standard tools in information processing are applied. RESULTS: We present an open three-axial mono-hierarchical nomenclature called SNOCAP-HET. Moreover, we explain other ideas to overcome the lack of systematization within the set of analysis methods suitable for HET. CONCLUSIONS: Our approach allows for an extension of SNOCAP-HET and will allow for the development and evaluation of different measures for the appropriateness of analysis methods given a certain highly intensive, multimodal and heterogeneous data set in the context of HET. Our final future aim is to obtain better results when analyzing medical data.

A collaboration tool based on SNOCAP-HET.

Health enabling technologies and ambient assisted living are important fields in biomedical informatics. In this context, a huge variety of analysis methods are applied. Neither is a suitable structuring of these methods available, nor is an aid known for selecting appropriate methods for a given set of data specifying a context and a problem. The goal of the present paper is to present a prototype of a semantic collaboration tool which is based on the Systematic Nomenclature for Contexts, Analysis Methods and Problems in Health-Enabling Technologies (SNOCAP-HET). This tool can be seen as a first step towards an assistance system for decision support within SNOCAP-HET. We present aspects of the selection and modeling process of our tool and discuss its benefits and appealing tasks for further research. Moreover we present a number of already planned and some unspecified upcoming steps which should optimize SNOCAP-HET in the future.

High intensity, multimodality and incoherence: grand challenges in the analysis of data for health-enabling technologies.

When working with health-enabling technologies, researchers all over the world usually have to analyze highly intensive, multimodal and incoherent data. We explain that there is a lack of systematization within the set of methods of analysis suitable for these data. As a first step towards a methodology in this context, we present the Systematic Nomenclature for Contexts, Analysis Methods and Problems in Health-Enabling Technologies (SNOCAP-HET).

Design and implementation of the standards-based personal intelligent self-management system (PICS).

Against the background of demographic change and a diminishing care workforce there is a growing need for personalized decision support. The aim of this paper is to describe the design and implementation of the standards-based personal intelligent care systems (PICS). PICS makes consistent use of internationally accepted standards such as the Health Level 7 (HL7) Arden syntax for the representation of the decision logic, HL7 Clinical Document Architecture for information representation and is based on a open-source service-oriented architecture framework and a business process management system. Its functionality is exemplified for the application scenario of a patient suffering from congestive heart failure. Several vital signs sensors provide data for the decision support system, and a number of flexible communication channels are available for interaction with patient or caregiver. PICS is a standards-based, open and flexible system enabling personalized decision support. Further development will include the implementation of components on small computers and sensor nodes.

Analysis of a mathematical model for the growth of cancer cells.

In this paper, a 2D model for the growth of multilayer tumours is presented. The model consists of a free boundary problem for the tumour cell membrane and the tumour is supposed to grow or shrink due to cell proliferation or cell dead. The growth process is caused by a diffusing nutrient concentration σ and is controlled by an internal cell pressure p. We assume that the tumour occupies a strip-like domain with a fixed boundary at y = 0 and a free boundary y = ρ(x), where ρ is a 2π-periodic function. First, we prove the existence of solutions (σ, p, ρ) on a scale of small Höolder spaces and show that our model allows for flat stationary solutions. As a main result, we establish that these equilibrium points are locally asymptotically stable under small perturbations.

Wearable sensors in healthcare and sensor-enhanced health information systems: all our tomorrows?

Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuropsychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs.

Necrotic tumor growth: an analytic approach.

The present paper deals with a free boundary problem modeling the growth process of necrotic multi-layer tumors. We prove the existence of flat stationary solutions and determine the linearization of our model at such an equilibrium. Finally, we compute the solutions of the stationary linearized problem and comment on bifurcation.

Assessment of cognitive workload of in-vehicle systems using a visual peripheral and tactile detection task setting.

The increase of driver information and infotainment systems includes also interaction technologies like speech interaction that minimize visual-manual demand and put the focus to cognitive demand. The question is whether this could lead to distraction effects and decreased traffic safety. This study presents an evaluation method for cognitive demand based on different detection paradigms in a dual task setting. A listening and a backward counting task are realized on three difficulty levels as simulations of cognitively loading secondary tasks and investigated using a visual versus a tactile detection paradigm. The results show that both detection paradigms are able to discriminate the task levels and that subjects successfully apply compensation strategies in the dual task setting especially during the listening task.