Measurement with an automated oscillometric wrist device with position sensor leads to lower values than measurements obtained with an automated oscillometric arm device from the same manufacturer in elderly persons.

OBJECTIVE: Home blood pressure (BP) monitoring is recommended by several clinical guidelines and has been shown to be feasible in elderly persons. Wrist manometers have recently been proposed for such home BP measurement, but their accuracy has not been previously assessed in elderly patients. METHODS: Forty-eight participants (33 women and 15 men, mean age 81.3±8.0 years) had their BP measured with a wrist device with position sensor and an arm device in random order in a sitting position. RESULTS: Average BP measurements were consistently lower with the wrist than arm device for systolic BP (120.1±2.2 vs. 130.5±2.2 mmHg, P<0.001, means±SD) and diastolic BP (66.0±1.3 vs. 69.7±1.3 mmHg, P<0.001). Moreover, a 10 mmHg or greater difference between the arm and wrist device was observed in 54.2 and 18.8% of systolic and diastolic measures, respectively. CONCLUSION: Compared with the arm device, the wrist device with position sensor systematically underestimated systolic as well as diastolic BP. The magnitude of the difference is clinically significant and questions the use of the wrist device to monitor BP in elderly persons. This study points to the need to validate BP measuring devices in all age groups, including in elderly persons.

The NLRP3 inflammasome: a sensor for metabolic danger?

Interleukin-1beta (IL-1beta), reactive oxygen species (ROS), and thioredoxin-interacting protein (TXNIP) are all implicated in the pathogenesis of type 2 diabetes mellitus (T2DM). Here we review mechanisms directing IL-1beta production and its pathogenic role in islet dysfunction during chronic hyperglycemia. In doing so, we integrate previously disparate disease-driving mechanisms for IL-1beta, ROS, and TXNIP in T2DM into one unifying model in which the NLRP3 inflammasome plays a central role. The NLRP3 inflammasome also drives IL-1beta maturation and secretion in another disease of metabolic dysregulation, gout. Thus, we propose that the NLRP3 inflammasome contributes to the pathogenesis of T2DM and gout by functioning as a sensor for metabolic stress.

Risk Evaluation for Spoofing against a Sensor Supplied with Liveness Detection

The aim of this paper is to evaluate the risks associated with the use of fake fingerprints on a livescan supplied with a method of liveness detection. The method is based on optical properties of the skin. The sensor uses several polarizations and illuminations to capture the information of the different layers of the human skin. These experiments also allow for the determination under which conditions the system is deceived and if there is an influence respectively of the nature of the fake, the mould used for the production or the individuals involved in the attack. These experiments showed that current multispectral sensors can be deceived by the use of fake fingerprints created with or without the cooperation of the subject. Fakes created from direct casts perform better than those produced by fakes created from indirect casts. The results showed that the success of the attack is influenced by two main factors. The first is the quality of the fakes, and by extension the quality of the original fingerprint. The second is the combination of the general patterns involved in the attacks since an appropriate combination can strongly increase the rates of successful attacks.

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Les champignons : essai de classification / par le Dr Paul Vuillemin,...

Collection : Bibliothèque de botanique cryptogamique

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Desenvolupament d'un node sensor sense fils autoalimentat. Mòdul piezo-magnètic

En aquest projecte es pretén implementar un dispositiu capaç de ser auto-suficient i no dependre de cap tipus de pila, bateria o fil elèctric que l’abasteixi d’energia elèctrica. El dispositiu recol·lectarà la energia magnètica generada per la corrent elèctrica a un fil i la transformarà en energia elèctrica, que serà emmagatzemada per el seu posterior ús. A demès, aquest projecte s’ha desenvolupat en col·laboració amb un segon projecte, dintre del qual s’implementarà una xarxa de sensors, mitjançant el protocol MIWI. Aquest projecte es divideix en tres grans blocs. El primer bloc del projecte serà una introducció teòrica de tots els coneixements relacionats amb el concepte d’energy harvesting i els mecanismes físic implicats. Al segon bloc podrem veure com s’han realitzat els càlculs, simulacions i posada en marxa, dels diferents elements que formaran el dispositiu recol·lector d’energia. Per últim en el tercer bloc veurem el prototip ja implementat. Es valoraran els resultats obtinguts, i es veuran els temps que necessitarà per alimentar al microcontrolador.

Measurement of the dynamics in ski jumping using a wearable inertial sensor-based system.

Abstract Dynamics is a central aspect of ski jumping, particularly during take-off and stable flight. Currently, measurement systems able to measure ski jumping dynamics (e.g. 3D cameras, force plates) are complex and only available in few research centres worldwide. This study proposes a method to determine dynamics using a wearable inertial sensor-based system which can be used routinely on any ski jumping hill. The system automatically calculates characteristic dynamic parameters during take-off (position and velocity of the centre of mass perpendicular to the table, force acting on the centre of mass perpendicular to the table and somersault angular velocity) and stable flight (total aerodynamic force). Furthermore, the acceleration of the ski perpendicular to the table was quantified to characterise the skis lift at take-off. The system was tested with two groups of 11 athletes with different jump distances. The force acting on the centre of mass, acceleration of the ski perpendicular to the table, somersault angular velocity and total aerodynamic force were different between groups and correlated with the jump distances. Furthermore, all dynamic parameters were within the range of prior studies based on stationary measurement systems, except for the centre of mass mean force which was slightly lower.

Non-invasive and non-occlusive blood pressure estimation via a chest sensor.

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.

A combined ear sensor for pulse oximetry and carbon dioxide tension monitoring: accuracy in critically ill children.

IMPLICATIONS: A new combined ear sensor was tested for accuracy in 20 critically ill children. It provides noninvasive and continuous monitoring of arterial oxygen saturation, arterial carbon dioxide tension, and pulse rate. The sensor proved to be clinically accurate in the tested range.