In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a nationwide anthropometric survey of 3,997 subjects. The resulting head and face measurements were used to develop an anthropometric database detailing the face size distributions of respirator users using both traditional measurement methods and three-dimensional (3D) scanning systems. This database was used to establish fit test panels to be incorporated into NIOSH respirator certification and international standards. One of the panels developed, called the principal component analysis (PCA) panel, uses the first two principal components obtained from a set of 10 facial dimensions (age and race adjusted) and divides user population into five face-size categories. These 10 dimensions are associated with respirator fit and leakage and can predict the remaining face dimensions as well. Respirators designed to fit these panels are expected to accommodate more than 95% of the current U.S. civilian workers.

From the 3,997 subje

This dataset comprises craniometric and anthropometric dimensions from over 300 participants collected as part of the FACEFIT and FACEFIT2.0 projects. This data was collected and compiled according to ISO #7250-1 and 15535 using a 3D camera and standard calipers.

Deaths or serious injuries among emergency medical technicians (EMTs) and other ambulance occupants occur at a high rate during transport. According to a study by the National Institute for Occupational Safety and Health (NIOSH), EMTs and paramedics have higher fatality rates when compared to all workers, with forty-five percent of EMT deaths resulting from highway incidents, primarily due to vehicle collisions.1 Data from the National Highway and Traffic Safety Administration showed that among the persons killed in crashes involving an ambulance between 1992 and 2011, twenty one percent were EMTs and patients, while four percent were ambulance drivers.2 To reduce injury potential to the EMTs and other ambulance occupants, NIOSH, the Department of Homeland Security, the U.S. General Services Administration, and the National Institute of Standards and Technology, along with private industry partners, have committed to improving the workspace design of ambulance patient compartments for safe and effective perfo

The most recent version of the DINED-series was based on two sources. The first is the Geron-project 1993-1998 of the TU Delft Ergonomics group (see also the Geron 1998 table), which provides the basic body dimensions. Second source is the Caesar-project from TNO Human Factors Soesterberg, which provides the circumferences. (2004)

This dataset tracks the updates made on the dataset "Anthropometric Database for the EMTs in the United States" as a repository for previous versions of the data and metadata.

This data was measured in 1982 in cooperation with Gemeentelijke Dienst Verpleging en Verzorging, the association for elderly care and housing in The Hague. (1984)

This study contains measurements of 347 Dutch students taking part in the first year of the Industrial Design Engineer Bachelor's programme at TU Delft. Measurements were conducted in 2014 in the traditional way, with calipers and tape in combination with a measurement chair. (2016)

Anthropometric data.

The TU Delft Geron project was a national study in which 750 subjects, who lived independently, were assessed. In total about 80 variables, all more or less important for product use, were measured. The sample consisted of four age groups ranging from 50 to over 80 years of age; a group of young people (20 - 30 years) was also studied for the purpose of comparison (see DINED 2004 table). Women and men participated in about equal numbers. (1998)

The dataset is of 10,782 children and adolescents, aged 2 to 19 years, who belonged to four major cities of Pakistan including Lahore, Multan, Rawalpindi/Islamabad. The dataset consists of data about variables age (years), gender status (boys/girls), residential city (Multan/ Lahore/ Rawalpindi or Islamabad) and anthropometric measurements i.e., height (0.1cm), weight (kg.), WC (0.1cm), HpC (0.1cm), MUAC (0.1cm), NC (0.1cm) and WrC (0.1cm).

This study is the result of a third year practicum with Delft Industrial Design Engineering students, measurements on students are done in three successive years.

Anthropometric data in 10 subjects.

Anthropometric data of the subjects.

The dataset contains recent anthropometric measurements data useful for updating ergonomic and safety specifications for fire apparatus and firefighter personal protective equipment (PPE). Data collected during the anthropometric measurements performed and from the consecutive analysis done on primary data is segregated by gender of individuals, posture and wearing or not of the gear during measurements and as well by the type of units (metric or imperial).

The accommodation of worker anthropometric variability in the workplace and personal protective equipment (PPE) is key to safe and efficient completion of work tasks. Previously, the best data available was 46 years old, which has largely become outdated due to demographic changes. These data tables consist of 34 traditional semi-nude body dimensions without gear (e.g., chest depth, standing; foot breadth, horizontal, standing; hip circumference; stature; elbow rest height, sitting; and eye height, sitting) and 15 dimension measurements over clothing and with gear (e.g., abdominal extension depth, sitting; hip breadth, sitting; and should-grip length, sitting) of 756 male and 218 female Law Enforcement Officers (LEOs). For many LEOs, patrol vehicles are the workplace where they spend significant portions of their workday and PPE is vital gear to safeguard LEOs from the harm of assaults. Design improvements of vehicle console space, vehicle ingress/egress, and LEO body-worn equipment can result in reduced LEO fatigue, pain, or injury.

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...

This table contains anthropometric data for 50th percentile U.S. male. This data has been used to calculate dimensions of truncated ellipsoidal finite element segments.

This dataset tracks the updates made on the dataset "NIOSH Anthropometric Data and ISO Digital Headforms" as a repository for previous versions of the data and metadata.

Subjets’ anthropometric data [Mean (SD)].

This data represents the raw data captured in the 2015 Anthropometric Survey of the Royal Australian Navy (ASRAN), as well as the data embedded in the Multivariate Virtual Fit Test. The ASRAN data provides 87 measurements, collected digitally and manually on 1322 Permanent Royal Australian Navy (RAN) personnel (232 females and 1090 males), aged 18–54 years. It can be used for the design and evaluation of vessels, systems, equipment and clothing. This data can be used to conduct multivariate anthropometric analyses and to determine precise levels of fit and accommodation based on the anthropometry. Note that it is always recommended to consider the inclusion of secular change, personal equipment and clothing correction factors, and other allowances that should be considered when using the anthropometric data (comfort, movement, posture etc). \r \r The Multivariate Virtual Fit Test has ASRAN data embedded in it, and supports the conduct of multivariate analyses. This tool developed by Prof Matthew Parkinson and Dr Matthew Reed can be used to predict accommodation in clothing, systems or platforms where body dimensions are independent of each other (e.g., how many people have a stature of x and a bideltoid breadth of y). It is very valuable to demonstrate the decreasing accommodation that occurs when using the historical 5th and 95th percentile values across multiple anthropometric dimensions.\r