Abstract
This article focuses on a sensorial glove prototype capable of acquiring hand motion and estimating its pose. The presented solution features twelve inertial measurement units (IMUs) to track hand orientation. The sensors are attached to a glove to decrease the project cost. The system also focuses on sensor fusion algorithms for the IMUs and further implementations, presenting the algebraic quaternion algorithm (AQUA), used because of its modularity and intuitive implementation. An adaptation of a human hand model is proposed, explaining its advantages and its limitations. Considering that the calibration is a very important process in gyroscope performance, the online and offline calibration data was analyzed, pointing out its challenges and improvements. To better visualize the model and sensors a simulation was conducted in Unity.

Abstract
Background: American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.Objective: We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. Methods: We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. Results: Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. Conclusions: Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. Trial Registration: PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339(J Med Internet Res 2022;24(9):e39452) doi: 10.2196/39452

Abstract

Abstract

American Academy of Sleep Medicine guidelines suggests that clinical prediction algorithms can be used to screen obstructive sleep apnea (OSA) patients without replacing polysomnography (PSG) – the gold standard.

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Abstract
The Mid-Infrared ELT Imager and Spectrograph (METIS) is one of the first generation science instruments on ESO's 39m Extremely Large Telescope (ELT). METIS will provide diffraction-limited imaging and medium resolution slit-spectroscopy from 3 - 13 microns (L, M, and N bands), as well as high resolution (R similar to 100,000) integral field spectroscopy from 2.9 - 5.3 microns. Both imaging and IFU spectroscopy can be combined with coronagraphic techniques. After passing its preliminary design review (PDR) in May 2019, and the final design review (FDR) of its optical system in June 2021, METIS is now preparing for the FDR of its entire system in the fall of 2022, while the procurements of many optical components have already started. First light at the telescope is expected in 2028, after a comprehensive assembly integration and test phase. In this paper we focus mainly on the various design aspects, and present a status update on the final optical and mechanical design of METIS. We describe the conceptual setup of METIS, its key functional components, and the resulting observing modes. Last but not least, we present the expected scientific performance, in terms of sensitivity, adaptive optics, and high contrast imaging performance.