3-D Anthropometry Measurements of Human Body Surface

A Novel Tool for Computer-Aided Design

By Andy R. Terrel [source]

About this dataset

This survey utilizes the cutting-edge three-dimensional (3-D) surface anthropometry technology, which measures the outermost surface of the human body. These technologies are a breakthrough in measuring capabilities, as they can accurately record hundreds of thousands of points in three dimensions in only a few seconds. With this data, designers and engineers are able to use computer-aided design tools and rapid prototyping in conjunction with more realistic postures to create better designs for their target audience more effectively.

Surface anthropometry has many advantages over traditional measuring methods like rulers and tape measures: it helps reduce guesswork through its accuracy; it allows measurements to be taken long after a subject has left; it provides an efficient way to capture individuals while wearing clothing, equipment or any other accessories; each measurement is comparable with those collected by other groups regardless of who took them; and lastly, the system is non-contact so there’s no risk for discrepancies between different measurers.

Our survey will look at 3 dimensional body measurements such demographics like age, gender, reported height and weight as well as individual body parts such waist circumference preferred braid size cup size ankle circumference scye circumference chest circumferences hip height spine elbow length arm part lengths should get out seams sleeveinseam biacromial breadth bicristal breadth bustbusters cervical height chest – els interscye distance acromion Hight acromion radial length axilla heights elbow heights knee heights radial mation length hand late neck circumstance based these 3 dimes entails taken from our dataset Caesarz dot csv make sure you provide us with all the necessary information thank you

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is provided to help researchers, designers, engineers and other professionals in related fields use 3-D surface anthropometry technology to effectively measure the outer surface of the human body.

Using this dataset can enable you to capture hundreds of thousands of points in three-dimensions on the human body surface. This data provides insights into sizing, fitting and proportions of a range of different body shapes and sizes which can be incredibly useful for many purposes like fashion design or biomedical research.

To get started with this dataset it is helpful to become familiar with some basic terminology such as biacromial breadth (the distance between furthest points on left and right shoulder), bicristal breadth (waist width measurement) , kneem height (the vertical distance from hip joint center to kneecap), ankle circumference (measurement taken at ankle joint) etc. Knowing these measurements can help you better interpret and utilize the data provided in this survey.

Next up, you’ll want familiarise yourself with the various measurements given for each column in this dataset including: age (Integer) , num_children (Integer) , gender (String) , reported_height (Float) , reported_weight (Float) . & more Once ready dive into the data by downloading it into your chosen analysis tool - popular options including KNIME or R Studio! You’ll be able to explore correlations between size & shape metrics as well as discovering patterns between participants based on gender/age etc. Spend some time getting comfortable playing around with your chosen system & just keep exploring interesting connections! Finally if there's a specific use case you have don't forget that user-defined variables are also possible - so create variables when needed! Thanks so much for taking part in our survey & we wish you all best luck analyzing the data - we hope it's useful!

Research Ideas

• Developing web-based applications or online platforms for measuring body dimensions using 3D technology for custom clothing and equipment.
• Establishing anthropometric databases, allowing user to easily find measurements of all kinds of body shapes and sizes;
• Analyzing patterns between anthropometric measurements and clinical data such as BMI (body mass index) to benefit the understanding of human health status and nutrition needs

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [Dataset copyright by authors](http...

Units of Measurement

This CODMAC level 3 data set contains the key parameters of the Inertial Measurement Package. In particular, it provides information on the gyroscope attitude measurements on a global scale and individual. It covers the period from launch in 2004, through the 3 Earth and 1 Mars flyby, plus the hibernation phases, plus the asteroid flybys and finally covers the Prelanding, comet escort & Extension phases of the prime target of the mission. The prime target is comet 67P/Churyumov-Gerasimenko 1 (1969 R1). This version V1.0 is the first version of this dataset.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters. A README.doc file is included to describe the measurement apparatus.

Key performance parameters measured during the field demonstration such as lining thickness, compressive strength, Flexural Strength, Modulus of Elasticity, bond Strength, Density, Set/Cure Time, and Slump. This dataset is associated with the following publication: Matthews, J., A. Selvakumar , S. Vaidya, and W. Condit. Large-Diameter Sewer Rehabilitation Using a Spray Applied Fiber Reinforced Geopolymer Mortar. Practice Periodical on Structural Design and Construction. American Society of Civil Engineers (ASCE), New York, NY, USA, 20(4): 9999, (2015).

Data repository for solar measurements from 4 WB funded stations in Armenia. The four solar measuring stations and the associated measurement campaign have been financed by the Scaling-Up Renewable Energy Program (SREP) as part of the preparation activities for the Armenia Utility-Scale Solar Project. This project, which is being jointly supported by SREP and the World Bank, will deliver the first utility-scale solar plant in the country. The locations for the measuring stations were selected by the Renewable Resources and Energy Efficiency Fund, the project’s implementing entity, following the recommendations from Effergy, the expert consultant firm. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/

To ensure meaningful comparison of test scores across groups or time, measurement invariance (i.e., invariance of the general factor structure and the values of the measurement parameters) across groups or time must be examined. However, many empirical examinations of measurement invariance of psychological/educational questionnaires need to address two issues: Using the appropriate model for ordinal variables (e.g., Likert scale items), and handling missing data. In two Monte Carlo simulations, this study examined the performance of one full-information-maximum-likelihood-based method and five multiple-imputation-based methods to obtain tests of measurement invariance across groups for ordinal variables that have missing data. Our results indicate that the full-information-maximum-likelihood-based method and one of the multiple-imputation-based methods generally have better performance than the other examined methods, though they also have their own limitations.

We collected grapevine shoot growth over a growing season of 2024 (April to June) in a vineyard of the horticultural unit 2 farm of the Ohio State University (40.73866822022149, -81.90273359323078). The measurements were made with a measuring tape.

State capacity is a core concept in political science research, and it is widely recognized that state institutions exert considerable influence on outcomes such as economic development, civil conflict, democratic consolidation, and international security. Yet, researchers across these fields of inquiry face common problems involved in conceptualizing and measuring state capacity. In this article, we examine these conceptual issues, identify three core dimensions of state capacity, and develop the expectation that they are mutually supporting and interlinked. We then use Bayesian latent variable analysis to estimate state capacity at the conjunction of indicators related to these dimensions. We find strong interrelationships between the three dimensions and produce a new, general-purpose measure of state capacity with demonstrated validity for use in a wide range of empirical inquiries. It is hoped that this project will provide effective guidance and tools for researchers studying the causes and consequences of state capacity.

This is the dataset that was used to make the figures for the publication entitled "Multi-year deployment of a Single Frequency High-Frequency acoustic attenuation system for measuring fine suspended sediments in stream channels."How the dataset was generated: A single frequency acoustic attention system was deployed for over three years in the Goodwin Creek Experimental Watershed in Panola County, MS, USA, to measure suspended fine sediments (d

https://humanrightsmeasurement.org/

Find more Pacific data on PDH.stat.

Data repository for measurements from a wind measurement station with a lidar in Bangladesh. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values. Please refer to the country project page for additional outputs and reports, including the installation reports: http://esmap.org/re-mapping/bangladesh. For access to maps and GIS layers, please visit the Global Wind Atlas: https://globalwindatlas.info/

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups.

This dataset contains Mini Surface Atmospheric Measurement Systems (MINISAMS) data for the Instabilities, Dynamics and Energetics accompanying Atmospheric Layering (IDEAL) project. These are the meteorological data from Dugway Proving Ground tower network. The data file is in comma separated ASCII value format. An Excel (.xls) file with each site's latitude, longitude and elevation is also included.See the readme for further information.

This list includes detail information about the measurement devices and measurement methods associated with the diversion and storage of water as reported annually for water rights as stored in the State Water Resources Control Board's "Electronic Water Rights Information Management System" (EWRIMS) database. All water right holders are required to submit an annual report including information related to the measurement devices and measurement methods associated with the diversion or storage of water. Each row correspond with a unique annual report-water right id-and measurement device ID combination and its associated data. This file is in flat file format and may not include all information associated to a water right such all uses and seasons or the amounts reported used for every month. Other information may be available in the associated flat files for each category. Examples of annual reports templates are provided as supporting information.

The impedance mesaurement data of two interconnected systems: the first one is a cascaded system composed of a boost converter and a buck converter; the second one is a paralleled system composed of two LCL converter.

1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as: (i) ordinations of the units, where interpoint distances optimally display the measured differences; (ii) clustering the units into homogeneous clusters; or (iii) assessing differences between pre-specified groups of units (e.g., regions, periods, treatment-control groups). 2. These methods all conceal a fundamental question: To what extent are the differences between the sampling units, computed according to the chosen proximity function, capturing the "size" in the multivariate observations, or their "shape"? "Size" means the overall level of the measurements: for example, some samples contain higher total abundances or more biomass, others less. "Shape" mea...

Data repository for measurements from 12 wind masts in Pakistan. Data transmits daily reports for wind speed, wind direction, air pressure, relative humidity and temperature. Please refer to the country project page for additional outputs and reports, including the installation reports: http://esmap.org/node/3058. For access to maps and GIS layers, please visit the Global Wind Atlas: https://globalwindatlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP).

Dataset contains all raw data measured with our developed sensor and two other for evaluation.

UWB Positioning and Tracking Data Set UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments. # Measurement system The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag. Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies. All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section. # Folder structure Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg. Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously. The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 6 subfolders: - cir_min_max_mean - positioning_wls - range - range_error - range_error_histograms - rss The organization of the data set is the following: data_set + location0 - anchors.csv - data.json - walking_path.csv + floorplan - floorplan.dxf - floorplan.png - floorplan_track.jpg - walking_path.csv + location1 - ... + location2 - ... + location3 - ... + raw_data + location0 + data + 1.07_9.37_1.2 - ch1_A1.csv - ch7_A8.csv - ... + 1.37_9.34_1.2 - ... + ... + location1 + ... + location2 + ... + location3 + ... + technical validation + cir_min_max_mean + positioning_wls + range + range_error + range_error_histograms + rss - LICENSE - README # Data format Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are: TAG_ID ANCHOR_ID X_TAG Y_TAG Z_TAG X_ANCHOR Y_ANCHOR Z_ANCHOR NLOS RANGE FP_INDEX RSS RSS_FP FP_POINT1 FP_POINT2 FP_POINT3 STDEV_NOISE CIR_POWER MAX_NOISE RXPACC CHANNEL_NUMBER FRAME_LENGTH PREAMBLE_LENGTH BITRATE PRFR PREAMBLE_CODE CIR (starts with this column; all columns until the end of the line represent the channel impulse response) # Availability of CODE Code for data analysis and preprocessing of all data available in this data set is published on GitHub: https://github.com/KlemenBr/uwb_positioning.git The code is licensed under the Apache License 2.0. # Authors and License Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si. Copyright (C) 2021 SensorLab, Jožef Stefan Institute, sensorlab@ijs.si. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. # Funding The research leading to these results has received funding from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116.