The iBalance SmartTouch software automates the performing of two different kind of balance tests. The tests include standard Romberg test with stable and unstable platform as well as Limits Of Stability. With the iBalance SmartTouch software results can be stored in the database for later analysis or they can be integrated with HUR SmartTouch solution to provide access to the analysis from the HUR SmartTouch web application. The iBalance SmartTouch is designed to work with the trapezoid BTG4, BT4, and BT3 Balance Platforms.
In addition to the testing side the user is also able to perform a variety of trainings ranging from maze to chase trainers where the goal is to improve your balance. As a new feature in the software the user is also able to play a Tux Racer game using the balance platform.
tanita bc 418 software download
The HUR Labs Body Composition Analyser software suite has been designed to be used together with Tanita body composition analysers. The BCA-software suite will speed up the testing and allow saving of results to the PC. It is easy to compare test results over time and motivate the customer to e.g weight control and exercise. The results can be printed out using your regular printer. The reports feature both numerical and graphical results compared to normative data. Using the BCA-software suite you can store and compare e.g. the following values ( recorder values depend on the analyser model)
ThePerformance Recorder software is a powerful tool to store, analyse and compare tests made with your Performance Recorder as well as managing the group/person database. Performance Recorder Software Suite is designed to work with both Performance Recorder units 9100 and 9200.
Easily connected via RS232C or USB, the TIGMON Health Monitoring software is a database capable of storing client/patient information together with their measurements.It will also provide professional reports and printouts, trend analysis of composition measurements against time and much more.
Our GMON Pro - Health Monitoring software was developed especially for the demands of professional usage and so it is possible to extend and modify it individually, depending on the requirements of your costumer- or patient service. Also at GMON Pro the constant monitoring of the important body values and the early knowledge of dangerous changes in the health situation are foregrounded. Because of the modular software structure it is possible to extend GMON Pro by additional registers whenever you like. We are pleased to inform you about the possibilities here.
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Four female subjects, including an FPLD2 subject (LMNA R482Q), an FPLD3 subject (PPARG F388L), and two control subjects were selected for MRI and analysis. MRI scans of subjects were performed on a 1.5T GE MR Medical system, with 17 transaxial slices comprising a 51 mm section obtained in both the mid-calf and mid-thigh regions. Using ImageJ 1.34 n software, analysis of raw MR images involved the creation of a connectedness map of the subcutaneous adipose tissue contours within the lower limb segment from a user-defined seed point. Quantification of the adipose tissue was then obtained after thresholding the connected map and counting the voxels (volumetric pixels) present within the specified region.
Analysis of the MRI stack images and measurements of subcutaneous adipose tissue was done by a single observer using analysis protocols developed in our laboratories (Figure 3). For each MRI data set acquired, the subcutaneous adipose tissue volume was quantified using ImageJ version 1.34 n image analysis software [9], specifically utilizing the Connected Threshold Grower and Voxel Counter tools. Subcutaneous adipose tissue was defined as the adipose tissue that circulated the circumference of the lower limb, adjacent to the skin, as well as any connected adipose tissue that was infiltrated into the muscle.
Prior to image analysis and fat quantification, all raw images underwent a preprocessing stage using the auto-brightness tool in order to minimize background noise and improve the quality of the images as much as possible. Images were further standardized with a distance in pixels set at 1.00 pixel/mm and the image dynamic was reduced to an 8-bit type to match the requirements of the used software. Starting from a user-defined seed point within the subcutaneous adipose tissue in the image, the method utilized the Connected Threshold Grower tool to create a connectedness map of the volumetric contours of subcutaneous adipose tissue within the image stack. This map represented the strength of connectivity between the seed point in the subcutaneous region and every voxel (volumetric pixel) in the image stack. Total tissue and adipose tissue threshold value ranges were obtained by manually sampling the signal intensity in each image stack. Using this threshold selection mechanism, the connectedness map of subcutaneous adipose tissue was then thresholded to segment the volume to be analyzed. Finally, the contours of segmented subcutaneous adipose tissue were quantitated using the Voxel Counter tool, which produced the final output voxel count within the volumetric region.
This semi-automated method involved a Connected Threshold Grower tool which specified inclusion of only adipose tissue connected to the initial subcutaneous seed point. Based on this pilot study of FPLD patients, we observed very high intra- and inter-observer correlation values: r > 0.99 and >0.98, respectively. In addition to its reproducibility, the described method yields results quickly and accurately, with minimal user intervention. The method was limited by including only connected infiltrated adipose tissue. However, given the imprecise definition of subcutaneous adipose tissue in extremities, we elected to include the connected infiltrated adipose tissue in our calculations, again since this would require no user judgment and/or intervention, thus reducing another potential source of analytic variation. An additional limitation inherent in the ImageJ software, which does not affect reproducibility but affects image dynamic, is that of the 16-bit to 8-bit change to the image stacks prior to analysis. This reduction in image dynamic, which reduces resolution, is a common setback in medical image processing where similar general-purpose software libraries are used. Future development of the software to utilize original raw images would be advantageous in maintaining image integrity and reflecting more accurate analysis data acquired from quantification.
In summary, we report the use of MRI and image analysis software employing Connected Threshold Grower and Voxel Counter tools to help quantify lower extremity subcutaneous fat depots in patients with two molecular forms of partial lipodystrophy. We also showed that the measurements showed high intra- and inter-observer correlation in a small sample. Finally, the measurements could be compared statistically and thus confirmed the clinical impression that FPLD2 and FPLD3 differ with respect to the extent of subcutaneous fat loss; specifically, subcutaneous fat loss in the FPLD2 subject is greater than in the FPLD3 individual. Increasing the sample size of FPLD subjects in future studies will validate this interpretation. These tools can be applied immediately and might be useful in quantitative phenotype analysis of other forms of lipodystrophy and in less extreme disorders of fat redistribution or repartitioning, such as "garden variety" obesity, insulin resistance, or type 2 diabetes.
Statistical analysis was performed using SPSS (version 24, Armonk, NY, USA), R (Foundation for Statistical Computing, version 4.1, Vienna, Austria), and R-studio (Integrated development for R, Boston, MA, USA) software. Sampling days were excluded from the statistical analyses when the data loss exceeded 6% of a single day. Mean, standard deviation (SD) with minimum and maximum value were calculated for each continuous variable, and frequencies and proportions were calculated for categorical variables. When the distribution was asymmetric the median and interquartile range were reported. In addition, the 95% confidence intervals (CIs) of the mean were calculated for sleep parameters, the number of recorded days, the frequency of weekend days, and the CRL and yolk sac size with their GA at the measurements.
Physical behaviors will be exhaustively classified using the Acti4 software [42] into time spent sitting or lying (termed sitting below), standing (consisting of time spent standing still, or standing with slight movement), and active (consisting of time spent walking, running, stair climbing or cycling).
Methods: Twenty-eight male 10- to 11-year-old football players participated in the study, with an age range of 10 to 11 years (average age 10.39 years 0.49). The players performed the tests in abnormal training day. The variables assessed were: Body composition (Tanita BC 418-MA), blood pressure and heart rate (Visomat Comfort 20/40 arm blood pressure monitor), jump test (Abalakov jump on Optojump platform) and aerobic capacity test (Course Navette Test 20m, CN). The software used to perform the statistical analysis was SPSS Statistics 23.0.
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