Markus Falgenhauer

Robust and Trusted Crowd-Sourcing and Crowd-Tasking in the Future Internet

A great majority of the EU citizens already owns a cellular phone. An increasing part of these phones are smartphones with a broadband internet connection. This growing network of smart internet enabled devices could act as a dense sensing network, as well as a tool for individual informing and tasking of mobile citizens and volunteers.

Piloting the European Unified Patient Identity Management (EUPID) Concept to Facilitate Secondary Use of Neuroblastoma Data from Clinical Trials and Biobanking.

Data from two contexts, i.e. the European Unresectable Neuroblastoma (EUNB) clinical trial and results from comparative genomic hybridisation (CGH) analyses from corresponding tumour samples shall be provided to existing repositories for secondary use. Utilizing the European Unified Patient IDentity Management (EUPID) as developed in the course of the ENCCA project, the following processes were applied to the data: standardization (providing interoperability), pseudonymization (generating distinct but linkable pseudonyms for both contexts), and linking both data sources. The applied procedures resulted in a joined dataset that did not contain any identifiers that would allow to backtrack the records to either data sources. This provided a high degree of privacy to the involved patients as required by data protection regulations, without preventing proper analysis.

An eHealth System for Pressure Ulcer Risk Assessment Based on Accelerometer and Pressure Data

Pressure ulcers are a common skin disease which is associated with pain, reduced autonomy, social isolation, and reduced quality of life. There are several systems for monitoring of pressure ulcer-related risk factors on the market, but up to now no satisfactory solution is available, especially for people with medium pressure ulcer risk. We present a novel pressure ulcer risk assessment and prevention system, which combines the advantages of accelerometer and pressure sensors for monitoring pressure ulcer risk factors. Sensors are used for detection of repositionings of the person lying on the mattress. Sensor data are sent to a tablet where they are analysed and presented graphically. The system was evaluated in a long-term test at the homes of people of the target group. Results indicate that the system is able to detect movements of persons while lying in bed. Weak correlation in between mobility and Braden pressure ulcer risk was found (correlation factor = 0.31). From our data, long-term trends could be visualized as well as 24 h mobility profiles. Such graphical illustrations might be helpful for caregivers in order to optimize care of people with medium to high pressure ulcer risk.

Data Fusion in the Environmental Domain

Air quality and air pollution have a very large impact on human health. The sensitivity to different pollutants varies per person, therefore it is important that citizens can get personalised air quality information. The Personal Environmental Information System (PEIS) aims at delivering just that. The PEIS takes sensor data from several data providers and employs a service-oriented architecture to deliver these observations to the user through a smartphone application. The PEIS also uses scientific models to fuse the sensor data and create new, derived observations. To make scientific models that fuse sensor data fit better in a service-oriented architecture, a software framework called Fusion4Decision was developed. This framework is based on Open Geospatial Consortium standards and allows scientific models written in languages like MATLAB or R to be available as a web service.

Leg Edema Quantification for Heart Failure Patients via 3D Imaging

Heart failure is a common cardiac disease in elderly patients. After discharge, approximately 50% of all patients are readmitted to a hospital within six months. Recent studies show that home monitoring of heart failure patients can reduce the number of readmissions. Still, a large number of false positive alarms as well as underdiagnoses in other cases require more accurate alarm generation algorithms. New low-cost sensors for leg edema detection could be the missing link to help home monitoring to its breakthrough. We evaluated a 3D camera-based measurement setup in order to geometrically detect and quantify leg edemas. 3D images of legs were taken and geometric parameters were extracted semi-automatically from the images. Intra-subject variability for five healthy subjects was evaluated. Thereafter, correlation of 3D parameters with body weight and leg circumference was assessed during a clinical study at the Medical University of Graz. Strong correlation was found in between both reference values and instep height, while correlation in between curvature of the lower leg and references was very low. We conclude that 3D imaging might be a useful and cost-effective extension of home monitoring for heart failure patients, though further (prospective) studies are needed.

A Patient Centered Sensor System for Decubitus Risk Predection and Prevention

Decubitus is a skin disease that is common in many care settings. State-of-the-art decubitus prevention focuses mainly on special decubitus mattresses, which are indicated for extremely high risk only, while existing systems for medium risk are hardly used due to low benefit and/or high costs. Therefore, a novel, unobtrusive monitoring system for decubitus prevention has been developed, consisting of five 3D accelerometers and four pressure sensors. A tablet visualized the amount of the person’s movements and estimated the individual decubitus risk. The system was tested in a feasibility study in autumn 2012. Results indicate that the system is usable and can provide useful information for decubitus prevention. Future work will include a long-term field study, evaluating the system when used by 30 medium-risk patients.

Validation of Different Parameters Extracted from 3D Images for Leg Oedema Detection

Heart failure patients discharged after cardiac decompensation are often re-admitted to hospital within several weeks. It is known that most of these early readmissions are avoidable, but even state-of-the-art home monitoring shows limited success rates. Therefore, innovative sensor techniques for home-monitoring are needed. Early detection of leg oedema might improve current home-monitoring approaches. We compared different methods for extraction of oedema related parameters from 3D images. Best performing parameters showed correlation factors of up to 0.58 as compared to body weight changes which indicates that 3D oedema detection could significantly improve home monitoring of heart failure patients, leading to less severe events and higher quality of life for affected patients.