Automatic measurement of hand dimensions using consumer 3D cameras

This article describes the metrological characterisation of two prototypes that use the point clouds acquired by consumer 3D cameras for the measurement of the human hand geometrical parameters. The initial part of the work is focused on the general description of algorithms that allow for the derivation of dimensional parameters of the hand. Algorithms were tested on data acquired using Microsoft Kinect v2 and Intel RealSense D400 series sensors. The accuracy of the proposed measurement methods has been evaluated in different tests aiming to identify bias errors deriving from point-cloud inaccuracy and at the identification of the effect of the hand pressure and the wrist flexion/extension. Results evidenced an accuracy better than 1 mm in the identification of the hand’s linear dimension and better than 20 cm3 for hand volume measurements. The relative uncertainty of linear dimensions, areas, and volumes was in the range of 1-10 %. Measurements performed with the Intel RealSense D400 were, on average, more repeatable than those performed with Microsoft Kinect. The uncertainty values limit the use of these devices to applications where the requested accuracy is larger than 5 % (volume measurements), 3 % (area measurements), and 1 mm (hands’ linear dimensions and thickness)

Feasibility Study of Drone-Based 3-D Measurement of Defects in Concrete Structures

Recognition of defects in concrete structures, identification of cracks, concrete spalling, or other geometrical defects are important tools for structural damage detection. Defects in structures can include cracks, but also missing parts, due to the wear caused by weather or aging phenomena. These last types of defects in structures can be identified using red-green-blue (RGB) cameras, but the level of damage could be difficult to evaluate with 2-D images. In this sense, the application of 3-D reconstruction techniques can be helpful to determine the 3-D dimensions of spalling, swelling of concrete or the presence of visible steel parts of reinforced concrete. The use of drones for this type of measurement is very attractive for reducing the costs and time of measurement campaigns. However, the lack of accurate trajectory information and the vibrations affect the accuracy of 3-D measurements. In this article, a metrological characterization of measurement systems for the evaluation and recognition of defects in concrete structures is presented, starting from the acquisition of 3-D point clouds by low-cost time-of-flight (ToF) sensors, placed on drones. To evaluate the uncertainty of these systems, a mock-up with realistic defects was developed and characterized using a reference 3-D scanner. The 3-D reconstructions obtained via the selected sensors were used to evaluate the discrepancies of the 3-D shape compared to a ground truth model and the uncertainty of the selected scanners. The results show that, for all the defects tested, the standard deviation of the discrepancies between the defect reconstructed using the drone and the ground truth is below 2.5 mm

Uncertainty mitigation in drone-based 3D scanning of defects in concrete structures

The measurement of defects in a concrete structures is highly relevant to determine how maintenance interventions should be performed. However, it could be difficult and potentially dangerous to inspect a certain structure by bringing trained operators, to places that are difficult to access. This issue could be overcome by framing the parts of interest of a building with a drone equipped with cameras. Nonetheless, a quantitative measure of a defect cannot be obtained with 2D cameras, since the pixel to millimeters scale and the estimation of depth are missing. To obtain a 3D shape measurement of a defect, 3D scanners, joined with 3D reconstruction, could be applied. In this article, we present a metrological evaluation of low-cost Time-Of-Flight (ToF) sensors for defects in concrete structures measurement. The defects of interest for this class of 3D scanners are mainly related to concrete spalling. This type of scanners was assembled on a drone with an onboard acquisition system. The testing benchmark for this study is based on a real structure with concrete spalling defects. A ground truth 3D model was obtained with a high-precision 3D scanner, used with a scaffolding. The effect of disturbances on measures were investigated, as well. The results of drone tests show that the systematic error of the 3D reconstruction with the selected sensors is about 0.5-2 mm, with a dispersion of raw data around the 3D reconstruction of about 2-4.5 mm, at a distance from the target of about 1.8-2 m

Virtual simulation benchmark for the evaluation of simultaneous localization and mapping and 3D reconstruction algorithm uncertainty

Simultaneous localization and mapping (SLAM) algorithms allow us to obtain a unique 3D shape and 3D sensor trajectory by combining partial scans obtained by moving a 3D scanner. The performances of these algorithms are significantly affected by experimental conditions, characteristics of the target and values of the parameters of the reconstruction algorithm. Therefore, the uncertainty and reliability of SLAM techniques need to be assessed before their application, e.g. for robot navigation, autonomous vehicles or industrial fields. To evaluate the uncertainty of these algorithms, specific datasets containing 3D scans, with the possibility to control different conditions, e.g. sensor trajectory, depth or color noise, sensor velocity and framerate, are necessary. In this article, we present a procedure to obtain virtual datasets with complete control of the environment, 3D sensor and trajectory conditions, starting from any real 3D dataset acquisition, characterized by a sufficiently low uncertainty. These datasets can be generated to test the effect of SLAM algorithm parameters to determine the best parameters to be used to exploit the algorithm characteristics to obtain the best result in each operating context. The advantage of this procedure is the possibility to perfectly control each condition and to evaluate its effect on the final result. This procedure was applied to two reconstruction algorithms as examples; namely, the Open3D reconstruction tool and ElasticFusion. The results demonstrate that the setting of algorithm parameters, e.g. the tolerance on depth correspondence between frames or the number of fragments, or the change in number of frames acquired, can have a strong influence on the resulting 3D reconstruction and trajectory. Moreover, the effect of not closing the loop trajectory on reconstruction performance is quantified for different application scenarios

Liraglutide and Renal Outcomes in Type 2 Diabetes.

BACKGROUND: In a randomized, controlled trial that compared liraglutide, a glucagon-like peptide 1 analogue, with placebo in patients with type 2 diabetes and high cardiovascular risk who were receiving usual care, we found that liraglutide resulted in lower risks of the primary end point (nonfatal myocardial infarction, nonfatal stroke, or death from cardiovascular causes) and death. However, the long-term effects of liraglutide on renal outcomes in patients with type 2 diabetes are unknown. METHODS: We report the prespecified secondary renal outcomes of that randomized, controlled trial in which patients were assigned to receive liraglutide or placebo. The secondary renal outcome was a composite of new-onset persistent macroalbuminuria, persistent doubling of the serum creatinine level, end-stage renal disease, or death due to renal disease. The risk of renal outcomes was determined with the use of time-to-event analyses with an intention-to-treat approach. Changes in the estimated glomerular filtration rate and albuminuria were also analyzed. RESULTS: A total of 9340 patients underwent randomization, and the median follow-up of the patients was 3.84 years. The renal outcome occurred in fewer participants in the liraglutide group than in the placebo group (268 of 4668 patients vs. 337 of 4672; hazard ratio, 0.78; 95% confidence interval [CI], 0.67 to 0.92; P=0.003). This result was driven primarily by the new onset of persistent macroalbuminuria, which occurred in fewer participants in the liraglutide group than in the placebo group (161 vs. 215 patients; hazard ratio, 0.74; 95% CI, 0.60 to 0.91; P=0.004). The rates of renal adverse events were similar in the liraglutide group and the placebo group (15.1 events and 16.5 events per 1000 patient-years), including the rate of acute kidney injury (7.1 and 6.2 events per 1000 patient-years, respectively). CONCLUSIONS: This prespecified secondary analysis shows that, when added to usual care, liraglutide resulted in lower rates of the development and progression of diabetic kidney disease than placebo. (Funded by Novo Nordisk and the National Institutes of Health; LEADER ClinicalTrials.gov number, NCT01179048 .)