18 resultados para Home monitoring system

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

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In this work, we provide a passive location monitoring system for IEEE 802.15.4 signal emitters. The system adopts software defined radio techniques to passively overhear IEEE 802.15.4 packets and to extract power information from baseband signals. In our system, we provide a new model based on the nonlinear regression for ranging. After obtaining distance information, a Weighted Centroid (WC) algorithm is adopted to locate users. In WC, each weight is inversely proportional to the nth power of propagation distance, and the degree n is obtained from some initial measurements. We evaluate our system in a 16m-18m area with complex indoor propagation conditions. We are able to achieve a median error of 2:1m with only 4 anchor nodes.

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The potential effects of climatic changes on natural risks are widely discussed. But the formulation of strategies for adapting risk management practice to climate changes requires knowledge of the related risks for people and economic values. The main goals of this work were (1) the development of a method for analysing and comparing risks induced by different natural hazard types, (2) highlighting the most relevant natural hazard processes and related damages, (3) the development of an information system for the monitoring of the temporal development of natural hazard risk and (4) the visualisation of the resulting information for the wider public. A comparative exposure analysis provides the basis for pointing out the hot spots of natural hazard risks in the province of Carinthia, Austria. An analysis of flood risks in all municipalities provides the basis for setting the priorities in the planning of flood protection measures. The methods form the basis for a monitoring system that periodically observes the temporal development of natural hazard risks. This makes it possible firstly to identify situations in which natural hazard risks are rising and secondly to differentiate between the most relevant factors responsible for the increasing risks. The factors that most influence the natural risks could be made evident.

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In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

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Zur Versachlichung der Diskussion über die TK-Studie zum Effekt eines Qualitätsmonitorings in der ambulanten Psychotherapie hat der wissenschaftliche Beirat die Ergebnisse aus seiner Sichtweise dargestellt. Zur Hauptfragestellung wird der Abschlussbericht zitiert, der bestätigt, dass es sich um eine konfirmatorische Untersuchung handelte. Im Kern sollte sie die Hypothesen zur Überlegenheit des TK-Modells gegenüber dem Verfahren der Gutachterverfahren überprüfen. Beim TK-Modell handelt es sich um eine „Komplexintervention“, die aus mehreren Bausteinen bestand. Die Studienergebnisse lassen somit nur die Aussage zu, dass diese Komplexintervention in ihrer Kombination keine Überlegenheit gezeigt hat. Ob einzelne Bausteine Wirksamkeit hatten, bedarf weiterer Forschung. Schließlich werden das Repräsentativitäts- und das Selektivitätsproblem der Studie bzw. der verwertbaren Stichproben erläutert und mit Verweis auf die Literatur wird deren Relevanz dargelegt.

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The clinical demand for a device to monitor Blood Pressure (BP) in ambulatory scenarios with minimal use of inflation cuffs is increasing. Based on the so-called Pulse Wave Velocity (PWV) principle, this paper introduces and evaluates a novel concept of BP monitor that can be fully integrated within a chest sensor. After a preliminary calibration, the sensor provides non-occlusive beat-by-beat estimations of Mean Arterial Pressure (MAP) by measuring the Pulse Transit Time (PTT) of arterial pressure pulses travelling from the ascending aorta towards the subcutaneous vasculature of the chest. In a cohort of 15 healthy male subjects, a total of 462 simultaneous readings consisting of reference MAP and chest PTT were acquired. Each subject was recorded at three different days: D, D+3 and D+14. Overall, the implemented protocol induced MAP values to range from 80 ± 6 mmHg in baseline, to 107 ± 9 mmHg during isometric handgrip maneuvers. Agreement between reference and chest-sensor MAP values was tested by using intraclass correlation coefficient (ICC = 0.78) and Bland-Altman analysis (mean error = 0.7 mmHg, standard deviation = 5.1 mmHg). The cumulative percentage of MAP values provided by the chest sensor falling within a range of ±5 mmHg compared to reference MAP readings was of 70%, within ±10 mmHg was of 91%, and within ±15mmHg was of 98%. These results point at the fact that the chest sensor complies with the British Hypertension Society (BHS) requirements of Grade A BP monitors, when applied to MAP readings. Grade A performance was maintained even two weeks after having performed the initial subject-dependent calibration. In conclusion, this paper introduces a sensor and a calibration strategy to perform MAP measurements at the chest. The encouraging performance of the presented technique paves the way towards an ambulatory-compliant, continuous and non-occlusive BP monitoring system.

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Stratospheric ozone is of major interest as it absorbs most harmful UV radiation from the sun, allowing life on Earth. Ground-based microwave remote sensing is the only method that allows for the measurement of ozone profiles up to the mesopause, over 24 hours and under different weather conditions with high time resolution. In this paper a novel ground-based microwave radiometer is presented. It is called GROMOS-C (GRound based Ozone MOnitoring System for Campaigns), and it has been designed to measure the vertical profile of ozone distribution in the middle atmosphere by observing ozone emission spectra at a frequency of 110.836 GHz. The instrument is designed in a compact way which makes it transportable and suitable for outdoor use in campaigns, an advantageous feature that is lacking in present day ozone radiometers. It is operated through remote control. GROMOS-C is a total power radiometer which uses a pre-amplified heterodyne receiver, and a digital fast Fourier transform spectrometer for the spectral analysis. Among its main new features, the incorporation of different calibration loads stands out; this includes a noise diode and a new type of blackbody target specifically designed for this instrument, based on Peltier elements. The calibration scheme does not depend on the use of liquid nitrogen; therefore GROMOS-C can be operated at remote places with no maintenance requirements. In addition, the instrument can be switched in frequency to observe the CO line at 115 GHz. A description of the main characteristics of GROMOS-C is included in this paper, as well as the results of a first campaign at the High Altitude Research Station at Jungfraujoch (HFSJ), Switzerland. The validation is performed by comparison of the retrieved profiles against equivalent profiles from MLS (Microwave Limb Sounding) satellite data, ECMWF (European Centre for Medium-Range Weather Forecast) model data, as well as our nearby NDACC (Network for the Detection of Atmospheric Composition Change) ozone radiometer measuring at Bern.

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The Oxford Programme for Immunomodulatory Immunoglobulin Therapy has been operating since 1992 at Oxford Radcliffe Hospitals in the UK. Initially, this program was set up for patients with multifocal motor neuropathy or chronic inflammatory demyelinating poly-neuropathy to receive reduced doses of intravenous immunoglobulin (IVIG) in clinic on a regular basis (usually every 3 weeks). The program then rapidly expanded to include self-infusion at home, which monitoring showed to be safe and effective. It has been since extended to the treatment of other autoimmune diseases in which IVIG has been shown to be efficacious.

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There is a demand for technologies able to assess the perfusion of surgical flaps quantitatively and reliably to avoid ischemic complications. The aim of this study is to test a new high-speed high-definition laser Doppler imaging (LDI) system (FluxEXPLORER, Microvascular Imaging, Lausanne, Switzerland) in terms of preoperative mapping of the vascular supply (perforator vessels) and postoperative flow monitoring. The FluxEXPLORER performs perfusion mapping of an area 9 x 9 cm with a resolution of 256 x 256 pixels within 6 s in high-definition imaging mode. The sensitivity and predictability to localize perforators is expressed by the coincidence of preoperatively assessed LDI high flow spots with intraoperatively verified perforators in nine patients. 18 free flaps are monitored before, during, and after total ischemia. 63% of all verified perforators correspond to a high flow spot, and 38% of all high flow spots correspond to a verified perforator (positive predictive value). All perfused flaps reveal a value of above 221 perfusion units (PUs), and all values obtained in the ischemic flaps are beneath 187 PU. In summary, we conclude that the present LDI system can serve as a reliable, fast, and easy-to-handle tool to detect ischemia in free flaps, whereas perforator vessels cannot be detected appropriately.

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PDZ-binding motifs are found in the C-terminal tails of numerous integral membrane proteins where they mediate specific protein-protein interactions by binding to PDZ-containing proteins. Conventional yeast two-hybrid screens have been used to probe protein-protein interactions of these soluble C termini. However, to date no in vivo technology has been available to study interactions between the full-length integral membrane proteins and their cognate PDZ-interacting partners. We previously developed a split-ubiquitin membrane yeast two-hybrid (MYTH) system to test interactions between such integral membrane proteins by using a transcriptional output based on cleavage of a transcription factor from the C terminus of membrane-inserted baits. Here we modified MYTH to permit detection of C-terminal PDZ domain interactions by redirecting the transcription factor moiety from the C to the N terminus of a given integral membrane protein thus liberating their native C termini. We successfully applied this "MYTH 2.0" system to five different mammalian full-length renal transporters and identified novel PDZ domain-containing partners of the phosphate (NaPi-IIa) and sulfate (NaS1) transporters that would have otherwise not been detectable. Furthermore this assay was applied to locate the PDZ-binding domain on the NaS1 protein. We showed that the PDZ-binding domain for PDZK1 on NaS1 is upstream of its C terminus, whereas the two interacting proteins, NHERF-1 and NHERF-2, bind at a location closer to the N terminus of NaS1. Moreover NHERF-1 and NHERF-2 increased functional sulfate uptake in Xenopus oocytes when co-expressed with NaS1. Finally we used MYTH 2.0 to demonstrate that the NaPi-IIa transporter homodimerizes via protein-protein interactions within the lipid bilayer. In summary, our study establishes the MYTH 2.0 system as a novel tool for interactive proteomics studies of membrane protein complexes.

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In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.