Fusing actigraphy signals for outpatient monitoring

[EN] Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.This work was partially funded by the European Commission: Help4Mood (Contract No. FP7-ICT-2009-4: 248765). E. FusterGarcia acknowledges Programa Torres Quevedo from Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-12-05693).Fuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015). Fusing actigraphy signals for outpatient monitoring. Information Fusion. 23:69-80. https://doi.org/10.1016/j.inffus.2014.08.003S69802

Untersuchung von Staphylococcus aureus Konzentrationen in einem definierten Aerosol mittels Impingement und Sedimentation

Wissenschaftlicher Hintergrund: Die Untersuchung der luftgebundenen Mikroorganismenkonzentration stellt in pharmazeutischen Firmen einen wichtigen Bereich zur Qualitätssicherung dar. Aufgrund der Tatsache, dass es bislang noch keine Richtlinie dafür gibt, welche Messmethode angewandt werden muss, wurden zwei Methoden gegenübergestellt und Korrelationen sowie Differenzen diskutiert. Methoden: In einer Aerosolkammer wurde die Anzahl der Keime bei zuvor bekannter Bakterienkonzentration unter standardisierten Bedingungen untersucht. Es wurde ein Vergleich zwischen einer aktiven sowie einer passiven Methode durchgeführt. Hierbei handelte es sich um das Impingement als aktive und die Sedimentation als passive Methode. Mit dem Impinger wurden für 30 min lang 12,5 L/min an Luftvolumen angesaugt und später in unterschiedlichen Mengen auf Nährmedien ausplattiert, während bei der Sedimentation eine Zeitspanne von 1 Stunde gewählt wurde. Bei beiden Verfahren wurde die Aerosolkammer zuvor mit einer Suspension einer bekannten Konzentration von Staphyloccocus aureus als Teststamm für eine Stunde geflutet. Die verwendeten Nährmedien CASO-Agar, COL-S und CNA-Agar wurden anschließend für 24 h bei 37 C bebrütet und die KBE gezählt. Ergebnisse: Der durchschnittliche Korrelationskoeffizient beider Methoden liegt bei 0,76, was auf einen eindeutigen Zusammenhang hinweist. Nach Annäherung beider Messmethoden auf KBE/m waren Impingement und Sedimentation im Verhältnis ähnlich gut. Auf CASO-Agar zeigte sich ein besserer Wert für die Sedimentation, auf CNA-Agar hingegen für das Impingement. Schlussfolgerung: Anhand der errechneten Ergebnisse kann zweifelsfrei gesagt werden, dass sich beide Methoden für die Sammlung von luftgetragenen Mikroorganismen eignen. Der Vergleich dieser zwei unterschiedlichen Methoden ist nur bedingt möglich, dennoch konnten repräsentative Ergebnisse erzielt werden. Mit welcher Methode letztlich gemessen wird, hängt von verschiedenen Bedingungen und Kostenfaktoren ab.Scientific background: The examination of airborne microorganism concentration is an important area for the quality assurance in pharmaceutical companies. Due to the fact that there is no directive on which measuring method has to be used, two methods were compared and correlations as well as differences were discussed. Methods: In an aerosol chamber, the number of germs was determined from known bacteria concentration under standardized conditions. A comparison between an active and a passive method were conducted. Impingement as an active method and sedimentation as a passive method were applied. The impinger was used to suck air for 30 minutes with a flowrate of 12.5 L/min and then plated in different amounts onto nutrient media, while a sedimentation period of 1 hour was chosen. In both methods, the aerosol chamber was previously flooded with a suspension of a known concentration of Staphyloccocus aureus as a test strain for one hour. The culture media CASO agar, COL-S and CNA agar were then incubated for 24 h at 37 C-, and the CFU were counted. Results: The average correlationcoefficiant of both methods is 0.76, indicating a clear correlation. A statistical calculation was made for both measurement methods to CFU/m, impingement and sedimentation were similar. On CASO agar a slightly better value for the sedimentation and on the CNA agar for the impingement was determined. Conclusion: This result show that both methods are suitable for the collection of airborne microorganisms for quality assurance. To compare these two different methods is only possible under certain conditions, nevertheless good results could be achieved. The method used for measurement of airborne microorganisms depends on different conditions and cost factors.Manuela BorotschnigZusammenfassungen in Deutsch und EnglischKarl-Franzens-Universität Graz, Diplomarbeit, 2017(VLID)214788

Evaluation of FRAX in patients with periprosthetic fractures following primary total hip and knee arthroplasty

Abstract The fracture risk assessment tool (FRAX) is a tool which calculates an individual 10-year fracture risk based on epidemiological data in patients with a risk of osteporosis. The aim of this study was to evaluate the value of FRAX to estimate the risk of postoperative periprosthetic fractures (PPF) in patients following with total hip and knee arthroplasty. 167 patients (137 periprosthetic fractures in total hip arthroplasty and 30 periprosthetic fractures in total knee arthroplasty) were included in this study. Patients’ data was retrieved retrospectively. In each patient the 10-year probability of a major osteoporotic fracture (MOF) and an osteoporotic hip fracture (HF) was calculated using FRAX. According to the NOGG guideline 57% of total hip arthroplasty (THA) patients and 43.3% of total knee arthroplasty (TKA) patients were in need of osteoporosis treatment, whereas only 8% and 7% received an adequate one respectively. 56% of the patients with PPF after THA and 57% of the patients with PPF after TKA reported about a previous fracture. Significant associations between the 10-year probability of a MOF and HF calculated by FRAX and PPF in THA and TKA were seen. The results of the present study show that FRAX might have the potential to estimate the PPF in patients following THA and TKA. FRAX should be calculated before and after THA or TKA in order to assess the risk and counsel patients. The data show a clear undertreatment of patients with PPF in respect to osteoporosis

Fuzzy Graph Tracking

. This paper is concerned with the problem of tracking groups of extended features in image sequences. A graph based image structure tracker is presented. The tracking is based on matching fuzzy features and feature relationships. Edges are extracted from subsequent frames. Fuzzy relational graphs are built from each image. The nodes of these graphs represent edge segments while the arcs code the relations among these segments. For each segment in one frame a set of potential assignments in the next frame is determined. Out of this assignment pool the global correspondence with highest similarity of features and feature relationships is calculated. Tracking is reduced to finding sets of mutually compatible nodes in graphs constructed from subsequent frames. The method is able to give guidelines for the correction of segmentation errors in particular frames. Keywords: tracking, fuzzy relations, graphs 1 Introduction Tracking is difficult because it requires to solve the correspondence..

Active Object Recognition in Parametric Eigenspace

We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to resolve the classification result obtained from a single view. The approach uses an appearance based object representation, namely the parametric eigenspace, and augments it by probability distributions. This captures possible variations in the input images due to errors in the pre-processing chain or the imaging system. Furthermore, the use of probability distributions gives us a gauge to view planning. View planning is shown to be of great use in reducing the number of images to be captured when compared to a random strategy. 1 Introduction Most computer vision systems found in the literature perform object recognition on the basis of the information gathered from a single image. Typically, a set of features is extracted and matched against object ..

Chaotic Behaviour of Hamiltonian Dynamical Systems. Study of a classical model of the hydrogen atom exposed to circularly polarized laser fields.

We have considered a classical model of the hydrogen atom exposed to a circularly polarized monochromatic electromagnetic wave. This model is related to similar models studied in various fields of physics, including the research in chaotic dynamics. The aim of the investigation has been to detect possible signs of stochasticity using the well established concept of Liapunov-exponents. The physical and mathematical background has been thoroughly explained and adaptions for the given problem have been made. The presented methods have been demonstrated using the H'enon-Heiles model and generalizations of it. The original problem has been simplified by a canonical transformation to the co-moving coordinate system of the lightwave. The properties of this transformation and it's quantum mechanical counterpart have been examined. Numerical methods have been used to integrate the classical equations of motion as well as the defining equations of the Liapunov exponents. In particular a time-tra..

Appearance-Based Active Object Recognition

We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to improve the classification result obtained from a single view. The approach uses an appearance based object representation, namely the parametric eigenspace, and augments it by probability distributions. This enables us to cope with possible variations in the input images due to errors in the pre-processing chain or changing imaging conditions. Furthermore, the use of probability distributions gives us a gauge to perform view planning. Multiple observations lead to a significant increase in recognition rate. Action planning is shown to be of great use in reducing the number of images necessary to achieve a certain recognition performance when compared to a random strategy. Keywords: action planning, object recognition, information fusion, parametric eigenspace,..

Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition

Abstract In this paper we introduce a formalism for optimal camera parameter selection for iterative state estimation. We consider a framework based on Shannon’s information theory and select the camera parameters that maximize the mutual information, i.e. the information that the captured image conveys about the true state of the system. The technique explicitly takes into account the a priori probability governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a posteriori probability at the current time step in the decision process as the a priori probability for the next time step. The convergence of the decision process can be proven. We demonstrate the benefits of our approach using an active object recognition scenario. The results show that the sequential decision process outperforms a random strategy, both in the sense of recognition rate and number of views necessary to return a decision.