7 research outputs found

    Depth-based 3D human pose refinement: Evaluating the refinet framework

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    In recent years, Human Pose Estimation has achieved impressive results on RGB images. The advent of deep learning architectures and large annotated datasets have contributed to these achievements. However, little has been done towards estimating the human pose using depth maps, and especially towards obtaining a precise 3D body joint localization. To fill this gap, this paper presents RefiNet, a depth-based 3D human pose refinement framework. Given a depth map and an initial coarse 2D human pose, RefiNet regresses a fine 3D pose. The framework is composed of three modules, based on different data representations, i.e. 2D depth patches, 3D human skeletons, and point clouds. An extensive experimental evaluation is carried out to investigate the impact of the model hyper-parameters and to compare RefiNet with off-the-shelf 2D methods and literature approaches. Results confirm the effectiveness of the proposed framework and its limited computational requirements

    Usefulness of transesophageal echocardiography in the assessment of aortic dissection

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    The acute dissection of the ascending aorta requires prompt and reliable diagnosis to reduce the high risk of mortality; in addition, prognosis is influenced by longterm complications. The aim of this article is to discuss transesophageal echocardiography (TEE) and (1) its diagnostic accuracy in the presurgical evaluation of patients, (2) its role in reducing time of diagnosis and surgery, and (3) its ability to reduce hospital mortality. TEE has also been tested as a screening method in the postsurgical follow-up of these patients. The retrospective investigation concerns a sample of 80 cases of acute dissection of the aorta, submitted for surgical intervention from April 1986 to February 1999. TEE has allowed a precise estimation of aortic diameters and optimal visualization of intimal flap and tear entry with a fine distinction between true and false lumen. A direct comparison of the results of TEE and of transthoracic echocardiography has demonstrated that some elements (visualization of flap and diameters in descending aorta, sites of entry and reentry, direction of let trough intimal tears, phasic intimal flap movement, diastolic collapse of flap on the valvular plane, false lumen thrombosis, coronary involvement, intramural hematoma, and aortic fissuration) were identified only by TEE, whereas other additional diagnostic elements (cardiac tamponade, aortic valve insufficiency, left ventricular function) show a similar pattern of significance. Routine employment of this method has confirmed a reduction of hospitalization time (about 1.5 hours of waiting time), and hospital mortality has changed from 42.8% to 17.3%. In the follow-up of patients operated on for aortic dissection, fundamental information may be obtained from TEE (assessment of the progression of thrombosis in the false lumen with its complete obliteration and modifications in aortic diameter with a consequent, possible worsening of aortic valve insufficiency). In conclusion, our study demonstrated that TEE may provide fast and efficient detection of acute aortic dissection. In the postsurgical follow-up, TEE has confirmed detection of major complications that can influence long-term prognosis and may be proposed as a method with easy access-one that is repeatable and inexpensive for the screening of aortic dissection surgical patients. (C) 2000 by Excerpta Medica, Inc

    SHREC 2022 track on online detection of heterogeneous gestures

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    This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features continuous sequences of hand tracking data where the gestures are interleaved with non-significant motions. The data have been captured using the Hololens 2 finger tracking system in a realistic use-case of mixed reality interaction. The evaluation is based not only on the detection performances but also on the latency and the false positives, making it possible to understand the feasibility of practical interaction tools based on the algorithms proposed. The outcomes of the contest's evaluation demonstrate the necessity of further research to reduce recognition errors, while the computational cost of the algorithms proposed is sufficiently low

    SARS-CoV-2 infection among hospitalised pregnant women and impact of different viral strains on COVID-19 severity in Italy: a national prospective population-based cohort study

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    OBJECTIVE: The primary aim of this article was to describe SARS-CoV-2 infection among pregnant women during the wild-type and Alpha-variant periods in Italy. The secondary aim was to compare the impact of the virus variants on the severity of maternal and perinatal outcomes. DESIGN: National population-based prospective cohort study. SETTING: A total of 315 Italian maternity hospitals. SAMPLE: A cohort of 3306 women with SARS-CoV-2 infection confirmed within 7 days of hospital admission. METHODS: Cases were prospectively reported by trained clinicians for each participating maternity unit. Data were described by univariate and multivariate analyses. MAIN OUTCOME MEASURES: COVID-19 pneumonia, ventilatory support, intensive care unit (ICU) admission, mode of delivery, preterm birth, stillbirth, and maternal and neonatal mortality. RESULTS: We found that 64.3% of the cohort was asymptomatic, 12.8% developed COVID-19 pneumonia and 3.3% required ventilatory support and/or ICU admission. Maternal age of 30-34 years (OR 1.43, 95% CI 1.09-1.87) and ≥35 years (OR 1.62, 95% CI 1.23-2.13), citizenship of countries with high migration pressure (OR 1.75, 95% CI 1.36-2.25), previous comorbidities (OR 1.49, 95% CI 1.13-1.98) and obesity (OR 1.72, 95% CI 1.29-2.27) were all associated with a higher occurrence of pneumonia. The preterm birth rate was 11.1%. In comparison with the pre-pandemic period, stillbirths and maternal and neonatal deaths remained stable. The need for ventilatory support and/or ICU admission among women with pneumonia increased during the Alpha-variant period compared with the wild-type period (OR 3.24, 95% CI 1.99-5.28). CONCLUSIONS: Our results are consistent with a low risk of severe COVID-19 disease among pregnant women and with rare adverse perinatal outcomes. During the Alpha-variant period there was a significant increase of severe COVID-19 illness. Further research is needed to describe the impact of different SARS-CoV-2 viral strains on maternal and perinatal outcomes

    Unsupervised Detection of Dynamic Hand Gestures from Leap Motion Data

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    The effective and reliable detection and classification of dynamic hand gestures is a key element for building Natural User Interfaces, systems that allow the users to interact using free movements of their body instead of traditional mechanical tools. However, methods that temporally segment and classify dynamic gestures usually rely on a great amount of labeled data, including annotations regarding the class and the temporal segmentation of each gesture. In this paper, we propose an unsupervised approach to train a Transformer-based architecture that learns to detect dynamic hand gestures in a continuous temporal sequence. The input data is represented by the 3D position of the hand joints, along with their speed and acceleration, collected through a Leap Motion device. Experimental results show a promising accuracy on both the detection and the classification task and that only limited computational power is required, confirming that the proposed method can be applied in real-world applications

    SHREC 2021: Skeleton-based hand gesture recognition in the wild

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    Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method
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