MAGES 4.0: Accelerating the world's transition to VR training and democratizing the authoring of the medical metaverse.

In this work, we propose MAGES 4.0, a novel Software Development Kit (SDK) to accelerate the creation of collaborative medical training applications in VR/AR. Our solution is essentially a low-code metaverse authoring platform for developers to rapidly prototype high-fidelity and high-complexity medical simulations. MAGES breaks the authoring boundaries across extended reality, since networked participants can also collaborate using different virtual/augmented reality as well as mobile and desktop devices, in the same metaverse world. With MAGES we propose an upgrade to the outdated 150-year-old master-apprentice medical training model. Our platform incorporates, in a nutsell, the following novelties: a) 5G edge-cloud remote rendering and physics dissection layer, b) realistic real-time simulation of organic tissues as soft-bodies under 10ms, c) a highly realistic cutting and tearing algorithm, d) neural network assessment for user profiling and, e) a VR recorder to record and replay or debrief the training simulation from any perspective.

6D Object Localization in Car-Assembly Industrial Environment.

In this work, a visual object detection and localization workflow integrated into a robotic platform is presented for the 6D pose estimation of objects with challenging characteristics in terms of weak texture, surface properties and symmetries. The workflow is used as part of a module for object pose estimation deployed to a mobile robotic platform that exploits the Robot Operating System (ROS) as middleware. The objects of interest aim to support robot grasping in the context of human-robot collaboration during car door assembly in industrial manufacturing environments. In addition to the special object properties, these environments are inherently characterised by cluttered background and unfavorable illumination conditions. For the purpose of this specific application, two different datasets were collected and annotated for training a learning-based method that extracts the object pose from a single frame. The first dataset was acquired in controlled laboratory conditions and the second in the actual indoor industrial environment. Different models were trained based on the individual datasets and a combination of them were further evaluated in a number of test sequences from the actual industrial environment. The qualitative and quantitative results demonstrate the potential of the presented method in relevant industrial applications.

Full-Body Pose Tracking-The Top View Reprojection Approach.

Recent introduction of low-cost depth cameras triggered a number of interesting works, pushing forward the state-of-the-art in human body pose extraction and tracking. However, despite the remarkable progress, many of the contemporary methods cope inadequately with complex scenarios, involving multiple interacting users, under the presence of severe inter- and intra-occlusions. In this work, we present a model-based approach for markerless articulated full body pose extraction and tracking in RGB-D sequences. A cylinder-based model is employed to represent the human body. For each body part a set of hypotheses is generated and tracked over time by a Particle Filter. To evaluate each hypothesis, we employ a novel metric that considers the reprojected Top View of the corresponding body part. The latter, in conjunction with depth information, effectively copes with difficult and ambiguous cases, such as severe occlusions. For evaluation purposes, we conducted several series of experiments using data from a public human action database, as well as own-collected data involving varying number of interacting users. The performance of the proposed method has been further compared against that of the Microsoft's Kinect SDK and NiTE (TM) using ground truth information. The results obtained attest for the effectiveness of our approach.

Sugar-added beverages consumption among kindergarten children of Crete: effects on nutritional status and risk of obesity.

OBJECTIVE: To assess the intake of sugar-added beverages such as soft drinks and commercially available fruit juices in kindergarten children, and to examine its association with obesity indices, physical activity levels and dietary habits. METHODS: A total of 856 children aged 4-7 years living in Crete, Greece in 2004-5 were included in this cross-sectional study. Nutrient and food intake was assessed with the use of 3-day weighed food records. Body measurements were used in order to assess BMI and waist circumference, and moderate-to-vigorous physical activity was calculated with the use of a questionnaire. RESULTS: Approximately 59.8% of all children consumed sugar-added beverages on a daily basis. High intake of sugar-added beverages (> 250 g/day) was associated with low intakes of calcium (p < 0.001), vitamin A and E (p < 0.010), fruits and vegetables (p = 0.007), and milk and yogurt (p = 0.048). Compared to non or low consumers, high consumers of sugar-added beverages (> 250 g/day) had higher BMI levels and two times greater risk of being overweight and/or obese (OR:2.35, p = 0.023). CONCLUSION: High intake of sugar-added beverages in kindergarten children is associated with poor eating habits and inadequate nutrient intake, as well as increased risk for developing childhood obesity.