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Journal articles on the topic 'Anthropometric data'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Tiit, Ene-Margit. "Creation and revitalization of the Estonian National Register of anthropometric data." Papers on Anthropology 25, no. 2 (December 21, 2016): 70. http://dx.doi.org/10.12697/poa.2016.25.2.07.

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Anthropmetric data have always been of interest for scientists. They have also great practical value for different groups of people: tailors, designers and also health care specialists. Anthropological data are different in different geographical areas and also change in time. That is why it is important to save also results of older anthropometrical measurements. In Estonia the anthropometrical measurements have been made by different researchers since the 18th century. The Group of Physical Anthropology at the University of Tartu (initiated by prof Helje Kaarma) was especially active in gathering anthropometric data. The data were saved in the Anthropometic Register. Unfortunately, the register had no financial support and so its activities stopped. In summer 2016 the group of people interested in saving the historical anthropometric data started revitalising the Register of Anthropological Data. Hopefully, it will be possible to use the Estonian Social Science Data Archive for this purpose.

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Dāboliņa, Inga, Ausma Viļumsone, and Eva Lapkovska. "ANTHROPOMETRIC PARAMETRIZATION OF UNIFORMS FOR ARMED FORCES." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 15, 2017): 41. http://dx.doi.org/10.17770/etr2017vol3.2519.

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Planning manufacture of uniforms decisions must be taken as to how many and in what size a particular model series should be manufactured, how they should be labeled and to what body-dimensions garment sizes should correspond. The purpose of anthropometric parametrization is to introduce garment size classification for mass production clothing, so as to representatively depict wearers’ body figure diversity. It is in the interests of manufacturers (design time and costs, logistics etc.) and buyers/procurement service alike to confine themselves with a minimum garment sizes and to use a possibly less complicated garment size classification. The aim of this research is to gather different impact factors for anthropometric parametrization for Uniforms of Armed Forces. Research is formed from anthropometric data and end user survey exploring data set of 150 soldiers. Anthropometrics are performed by non-contact anthropometric methods (3D anthropometrical scanner Vitus Smart XXL® is used in the study), data processing automation systems, pattern making CAD/CAM systems, etc. National Armed Forces (NAF) technical specification provides for manufacture of a wide range of garment sizes (by height indifference interval of 6 cm). Manufacturers labeling system and charts of finished products are subjected to comparative analysis and evaluation of corresponding standard recommendations is included. The study indicates the need to optimize the currently used anthropometry method in the Latvian Army, anthropometric data registration and application, thus improving the performance of military personnel and the procurement process and the use of resources, thus promoting resource planning and environmental protection. Also improvements are needed in uniform labeling and instructing of the military personnel on selection of uniform and equipment components. Proper anthropometric parametrization and labeling of Uniforms for Armed Forces would minimize expenses of Uniforms as well as the negative waste impact to the environment.

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Kinghorn, Rhonda A., and Alvah C. Bittner. "Truck Driver Anthropometric Data: Estimating the Current Population." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 9 (October 1993): 580–84. http://dx.doi.org/10.1177/154193129303700914.

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This report shows that a challenge facing designers of commercial trucks and other vehicles is a lack of current operator anthropometric data on which to base design decisions. Specifically, it was points out that current data suffer from a number of limitations including secular size changes, ethnic and gender composition shifts, and excessive standard errors (S.E.) of percentiles estimates. These and other limitations point out the need for estimates of contemporary, professional driver anthropometry. This report presents tabulations of comprehensive male and female driver population anthropometry estimates, and outlines a method for applying these anthropometric data to the design of trucks and other vehicles.

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Erkan, Ilker. "A system proposal for rapid detecting of anthropometric data and affecting design strategies." Journal of Engineering, Design and Technology 18, no. 6 (March 27, 2020): 1793–822. http://dx.doi.org/10.1108/jedt-11-2019-0302.

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Purpose The purpose of this study on architecture, design, ergonomics and anthropometry was to ensure compliance with the human-machine-work environment, minimize human error and obtain anthropometric measurements accurately, safely and rapidly. Design/methodology/approach The developed system efficiently extracted anthropometric data for 15,243 individuals with an accuracy rate of 98.8 per cent, focusing on the values for “shoulder breath” and “body depth.” In this study, a new anthropometric measurement system was developed and subsequently applied to obtain anthropometric measurements easily and quickly. The effect of the newly collected anthropometric data on the design discipline was evaluated. Findings The findings highlighted the need to update the anthropometric data used in other design studies. In addition to contributing to designing discipline, the updated anthropometric data are considered suitable for use in many different fields. Research limitations/implications The design discipline and related disciplines are expected to take advantage of these measurements. Updating the aforementioned data will also be easier and faster because of the simplicity and affordability of the system. Originality/value This is the first and only such study in Turkey with regard to the up-to-date anthropometric measurements obtained and the size of the database created.

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Shu, Chang, Stefanie Wuhrer, and Pengcheng Xi. "3D anthropometric data processing." International Journal of Human Factors Modelling and Simulation 3, no. 2 (2012): 133. http://dx.doi.org/10.1504/ijhfms.2012.051093.

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6

Hearps, Stephen J. C. "Self-reported anthropometric data." Canadian Journal of Public Health 101, no. 4 (July 2010): 345. http://dx.doi.org/10.1007/bf03405301.

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Zetli, Sri, Nofriani Fajrah, and Melanda Paramita. "PERBANDINGAN DATA ANTROPOMETRI BERDASARKAN SUKU DI INDONESIA." JURNAL REKAYASA SISTEM INDUSTRI 5, no. 1 (November 15, 2019): 23. http://dx.doi.org/10.33884/jrsi.v5i1.1390.

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Anthropometry is defined as the science of measurement and art in applying human physicalproperties, which is one of the most important factors to consider in designing a product. Productsthat meet ergonomic rules are products that are designed according to the dimensions of the user'sbody. Various factors affect anthropometric data, one of which is ethnicity. Ethnicity is defined as agroup of people identified through ancestral heritage, certain languages and certain cultures.Indonesia has more than 300 different ethnic groups, so it is important to represent anthropometricdata based on ethnicity. Batam City has a heterogeneous community consisting of various tribes andgroups. The dominant tribes include Batak, Javanese, Malay, Minang and Chinese. With the variationin anthropometric size, it is not possible for a design system to adjust to all types of sizes, thereforethere is a need for a database of anthropometric sizes in Indonesia. From the comparative testconducted by Anova testing, all 36 anthropometric with Sig. <α (0.05) which means that there aredifferences in Anthropometric between the five terms. Whereas for differences in AnthropometricBatak and Javanese are 19 same and 17 different, Batak and Malay are 14 same and 22 different,Batak and Minang are 12 same and 24 different, Batak and Chinese are 14 same and 22 different,Java and Malay are 12 same and 24 different, Java and Minang are 12 same and 24 different, Javaand Chinese are 12 same and 24 different, Malay and Minang are 9 same and 27 different, Malay andChinese are 14 same and 22 different, Minang and Chinese are 14 same and 22 different. The resultsof anthropometry obtained based on this research are expected to be a recommendation in thedevelopment of more ergonomic tools for users, especially the people of Indonesia.

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Paquette, Steven, J. David Brantley, Brian D. Corner, Peng Li, and Thomas Oliver. "Automated Extraction of Anthropometric Data from 3D Images." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 38 (July 2000): 727–30. http://dx.doi.org/10.1177/154193120004403811.

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The use of 3D scanning systems for the capture and measurement of human body dimensions is becoming commonplace. While the ability of available scanning systems to record the surface anatomy of the human body is generally regarded as acceptable for most applications, effective use of the images to obtain anthropometric data requires specially developed data extraction software. However, for large data sets, extraction of useful information can be quite time consuming. A major benefit therefore is to possess an automated software program that quickly facilitates the extraction of reliable anthropometric data from 3D scanned images. In this paper the accuracy and variability of two fully automated data extraction systems (Cyberware WB-4 scanner with Natick-Scan software and Hamamatsu BL Scanner with accompanying software) are examined and compared with measurements obtained from traditional anthropometry. In order to remove many confounding variables that living humans introduce during the scanning process, a set of clothing dressforms was chosen as the focus of study. An analysis of the measurement data generally indicates that automated data extraction compares favorably with standard anthropometry for some measurements but requires additional refinement for others.

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Albin, Thomas J. "Combining Very Limited Anthropometric Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, no. 1 (September 2013): 943–47. http://dx.doi.org/10.1177/1541931213571209.

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Lee, Young Suk, and Sung Heon Shin. "Anthropometric Data Application in Product Design." Japanese journal of ergonomics 40 (2004): 126–27. http://dx.doi.org/10.5100/jje.40.supplement_126.

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Koch, H. J., and C. Raschka. "Anthropometric data and acetylsalicylic acid pharmacokinetics." Int. Journal of Clinical Pharmacology and Therapeutics 40, no. 01 (January 1, 2002): 30–34. http://dx.doi.org/10.5414/cpp40030.

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Perumal, Nandita, Sorrel Namaste, Huma Qamar, Ashley Aimone, Diego G. Bassani, and Daniel E. Roth. "Anthropometric data quality assessment in multisurvey studies of child growth." American Journal of Clinical Nutrition 112, Supplement_2 (July 16, 2020): 806S—815S. http://dx.doi.org/10.1093/ajcn/nqaa162.

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ABSTRACT Background Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < −2SD] and wasting [weight-for-height z score (WHZ) < −2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition. Objectives We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status. Methods We used anthropometric data for children 0–59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time. Results Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index. Conclusions A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.

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Ulijaszek, Stanley J., and Deborah A. Kerr. "Anthropometric measurement error and the assessment of nutritional status." British Journal of Nutrition 82, no. 3 (September 1999): 165–77. http://dx.doi.org/10.1017/s0007114599001348.

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Anthropometry involves the external measurement of morphological traits of human beings. It has a widespread and important place in nutritional assessment, and while the literature on anthropometric measurement and its interpretation is enormous, the extent to which measurement error can influence both measurement and interpretation of nutritional status is little considered. In this article, different types of anthropometric measurement error are reviewed, ways of estimating measurement error are critically evaluated, guidelines for acceptable error presented, and ways in which measures of error can be used to improve the interpretation of anthropometric nutritional status discussed. Possible errors are of two sorts; those that are associated with: (1) repeated measures giving the same value (unreliability, imprecision, undependability); and (2) measurements departing from true values (inaccuracy, bias). Imprecision is due largely to observer error, and is the most commonly used measure of anthropometric measurement error. This can be estimated by carrying out repeated anthropometric measures on the same subjects and calculating one or more of the following: technical error of measurement (TEM); percentage TEM, coefficient of reliability (R), and intraclass correlation coefficient. The first three of these measures are mathematically interrelated. Targets for training in anthropometry are at present far from perfect, and further work is needed in developing appropriate protocols for nutritional anthropometry training. Acceptable levels of measurement error are difficult to ascertain because TEM is age dependent, and the value is also related to the anthropometric characteristics of the group or population under investigation. R > 0·95 should be sought where possible, and reference values of maximum acceptable TEM at set levels of R using published data from the combined National Health and Nutrition Examination Surveys I and II (Frisancho, 1990) are given. There is a clear hierarchy in the precision of different nutritional anthropometric measures, with weight and height being most precise. Waist and hip circumference show strong between-observer differences, and should, where possible, be carried out by one observer. Skinfolds can be associated with such large measurement error that interpretation is problematic. Ways are described in which measurement error can be used to assess the probability that differences in anthropometric measures across time within individuals are due to factors other than imprecision. Anthropometry is an important tool for nutritional assessment, and the techniques reported here should allow increased precision of measurement, and improved interpretation of anthropometric data.

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Hoekstra, Pyter N. "Predicting Centre-Dependent Spatial Density Functions of 3D Surface Anthropometry Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 38 (July 2000): 778–81. http://dx.doi.org/10.1177/154193120004403825.

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A new, valuable but vast dataset will become available to the scientific and general public: the results of the CAESAR project (Civilian American and European Surface Anthropometry Resource). The concept of centre-dependent spatial density functions was introduced earlier to realise a major reduction in surface anthropometry scan data. Here the basic mathematics are described of centre-dependent vector shifts needed to arrive at these spatial density functions. Since as yet no real 3D scan data are available to us the concepts are tested with a Monte Carlo generation of ninetytwo models with a reasonable variation in eight anthropometric variables and discussed.

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Wibneh, Amare, Ashish Kumar Singh, and Sougata Karmakar. "Anthropometric Measurement and Comparative Analysis of Ethiopian Army Personnel Across Age, Ethnicity, and Nationality." Defence Science Journal 70, no. 4 (June 25, 2020): 383–96. http://dx.doi.org/10.14429/dsj.70.15435.

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The anthropometric characteristics of the users depend upon the topography, nutrition, age, ethnicity, gender, and living conditions, and play a crucial role in the design of the equipment and the workspace to be used by them. This study aims to establish an anthropometry database for male Ethiopian army personnel and investigate the anthropometric variability across ethnicity, age, and with other countries, with the intention to facilitate ergonomic design and development of various facilities (e.g., equipment/ devices, and workspaces) for the Ethiopian army. Following the reliability assessment of the physical measurement technique, the anthropometric data from 250 Ethiopian male army personnel (four different ethnic groups at different age levels) were collected, and normality of the data set was tested. The anthropometric database of Ethiopian army personnel in terms of range, mean, standard deviation, percentile values (5th, 50th, and 95th) was documented. PCA was applied to select a smaller representative by reducing a larger set of variables for further statistical analysis and applications. ANOVA and follow-up posthoc test (Tukey’s HSD test) were carried out to compare anthropometric differences among different age groups and ethnic variations. The mean anthropometric differences were also compared with databases from other countries (India, Korea, and USA) using t-test. Significant variations were found when the anthropometry was compared between age, ethnicity, and cross-nationals. The findings indicate that variations in age, ethnicity, and geographical factors could have a significant impact on the ergonomic design of equipment and workspaces of Ethiopian army personnel.

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Niu, Jianwei, Zhizhong Li, and Gavriel Salvendy. "Alignment Influence on 3D Anthropometric Data Clustering." Ergonomics Open Journal 1, no. 1 (November 17, 2008): 62–66. http://dx.doi.org/10.2174/1875934300801010062.

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KUCZMARSKI, MARIE FANELLI, ROBERT J. KUCZMARSKI, and MATTHEW NAJJAR. "Descriptive Anthropometric Reference Data for Older Americans." Journal of the American Dietetic Association 100, no. 1 (January 2000): 59–66. http://dx.doi.org/10.1016/s0002-8223(00)00021-3.

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Kothiyal, Kamal, and Samuel Tettey. "Anthropometric data of elderly people in Australia." Applied Ergonomics 31, no. 3 (June 2000): 329–32. http://dx.doi.org/10.1016/s0003-6870(99)00052-6.

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Hiromi, Hiromi, Yunisa Astiarani, Robi Irawan, and Mariani Santosa. "Chair dimension compatibility with elementary school students anthropometric data in North Jakarta." Jurnal Biomedika dan Kesehatan 4, no. 1 (March 31, 2021): 12–18. http://dx.doi.org/10.18051/jbiomedkes.2021.v4.12-18.

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BACKGROUND In Indonesia, primary school begins at 6 years old and continues until 12, where most of their growth is experienced at that age. Non-ergonomic school furniture can harm the musculoskeletal system. This study evaluates the suitability of chair dimensions to elementary school student’s anthropometry in North Jakarta. METHODSA cross-sectional study of 98 students in North Jakarta. Chair dimension data and student anthropometry were measured using a tape measure, which was then analyzed using the Chi-Square Goodness of Fit Test to evaluate their suitability. RESULTSThe ages of the students ranged from 5 to 11 years. Anthropometric measurements of students show that the mean Sitting Shoulder Height is 41.81±4.36 cm, Popliteal Height 36.83±3.77 cm, Hip Breadth 25.88±3.47 cm, and Buttock-Popliteal Length 36.56±4.33 cm. While the average size assessed from the seat dimensions is Seat Height 41.71±0.22 cm, Seat Width 37.2±1.26 cm, Seat Depth 37.2±1.42 cm, and Backrest Height Above Seat 35.54±3.19 cm. The results of Goodness of Fit with Kendall's Tau-b critical value for the suitability of chair dimensions to student anthropometry were 0.37, and vice versa 0.672, which stated a discrepancy. CONCLUSIONThere is a mismatch between chair dimension and anthropometry of elementary school students in North Jakarta. Adjustment of chair dimensions needs to be done using a student's average size approach to prevent musculoskeletal disorders.

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Saluvere, Katrin, Jana Peterson, Liidia Saluste, and Säde Koskel. "Systematisation of anthropometric data of 17-year-old schoolgirls from Tartu, Estonia." Anthropologischer Anzeiger 56, no. 3 (September 22, 1998): 267–80. http://dx.doi.org/10.1127/anthranz/56/1998/267.

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Veldre, G., R. Stamm, and S. Koskel. "A possibility of systematisation of anthropometric data of girls aged 12-15." Anthropologischer Anzeiger 60, no. 4 (December 13, 2002): 369–82. http://dx.doi.org/10.1127/anthranz/60/2002/369.

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Azodo, Adinife Patrick, Olasunkanmi Salami Isamaila, and Sampson Chisa Owhor. "FITNESS AND COMFORT ASSESSMENT OF FOOTWEAR: AN ANTHROPOMETRIC APPRAISAL." Journal of Engineering Science 28, no. 3 (September 2021): 80–86. http://dx.doi.org/10.52326/jes.utm.2021.28(3).06.

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Suitability determination of any product designed for specific types of consumers is possible through the effective use of anthropometric information. This study assessed anthropometric data utilization in footwear designs and patterns as an indicator of fitness and comfort in footwear production. The data collected for analysis were the length and the breadth dimensions of footwear design pattern from eighteen footwear cottage shops and the foot anthropometric parameter from a total of four hundred and thirty-three (433) (males (226) and females (207)) subjects. The instrumentation design for the data collection was a digital vernier caliper (model Mitutoyo 500-506-10). The analysis of the foot anthropometry dimension and the design footwear pattern data obtained showed a lack of bilateral symmetry for the male and female gender. The fitness and comfortable foot support function of the footwear analyzed using a paired samples t-test between the footwear design pattern dimensions, and the foot anthropometric parameters disclosed p > 0.05 in all cases – not significant. This study concluded that tailoring a product design to the users’ population reduces the mismatch challenges, grants fitness, and comfort to the users.

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Stankeviča, Jekaterina, Liāna Pļaviņa, and Silvija Umbraško. "Anthropometrics parameters for Latvian women in the age over 40 years." Papers on Anthropology 26, no. 2 (September 18, 2017): 136. http://dx.doi.org/10.12697/poa.2017.26.2.14.

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The World Health Organization (WHO) recommends the anthropometrics parameters for the evaluation of overweight and obesity in adult population that is one of the risk factor for metabolic disorders and cardio-vascular pathology. The target of our study is to describe the variations of anthropometric parameters of the Latvian women population. The present paper includes the analysis of data of 200 women in the age 40–65 years. We evaluated the anthropometrics indicators in Latvian women in the age over 40 years, various somatometric measurements – height (cm), the body mass (kg), the circumference of the waist and hips (cm) – and calculated the mean parameters, the Body Mass Index and the Waist Hip Ratio. The respondents of the study group were divided into five age subgroups. We provided the analysis of anthropometric data, compared them with the data from other European countries. We have fixed the high prevalence of overweight and obesity in the examined women groups and proposed potential activities to reduce them.

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Albin, Thomas J. "Design with limited anthropometric data: A method of interpreting sums of percentiles in anthropometric design." Applied Ergonomics 62 (July 2017): 19–27. http://dx.doi.org/10.1016/j.apergo.2017.02.005.

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Yamaoka, Toshiki. "Making use of anthropometric data for product design." Japanese journal of ergonomics 30, Supplement (1994): 66–67. http://dx.doi.org/10.5100/jje.30.supplement_66.

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Gerver, W. J. M., and R. de Bruin. "Body composition in children based on anthropometric data." European Journal of Pediatrics 155, no. 10 (September 10, 1996): 870–76. http://dx.doi.org/10.1007/s004310050505.

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Mahmoudi, Nader, and Majid Bazrafshan. "A Carpet-Weaver’s Chair Based on Anthropometric Data." International Journal of Occupational Safety and Ergonomics 19, no. 4 (January 2013): 543–50. http://dx.doi.org/10.1080/10803548.2013.11077006.

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STEENBEKKERS, L. P. A., and J. F. M. MOLENBROEK. "Anthropometric data of children for non-specialist users." Ergonomics 33, no. 4 (April 1990): 421–29. http://dx.doi.org/10.1080/00140139008927146.

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Klein, Hendrik, and Klaus Broekel. "Orbit model from scanning data with anthropometric parameters." International Journal of Computer Applications in Technology 30, no. 3 (2007): 184. http://dx.doi.org/10.1504/ijcat.2007.015716.

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Sonego, Thiago, Jose Pinto, Luciana Godoy, Carolina Leal, and Marina Artico. "Anthropometric Data as a Predictor of OSA Severity." Otolaryngology–Head and Neck Surgery 143, no. 2_suppl (August 2010): P157. http://dx.doi.org/10.1016/j.otohns.2010.06.260.

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Makol, Neelam, Prateep Roy, and Salil Basu. "Heritability Estimates of Anthropometric Traits Using Twins Data." Journal of Human Ecology 2, no. 1 (January 1991): 93–94. http://dx.doi.org/10.1080/09709274.1991.11907704.

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†, James Barter, and Gilbert B. Forbes. "CORRELATION OF POTASSIUM-40 DATA WITH ANTHROPOMETRIC MEASUREMENTS*." Annals of the New York Academy of Sciences 110, no. 1 (December 15, 2006): 264–70. http://dx.doi.org/10.1111/j.1749-6632.1963.tb17091.x.

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Neves, Walter A., Francisco M. Salzano, and Fernando J. Da Rocha. "Principal-components analysis of Brazilian Indian anthropometric data." American Journal of Physical Anthropology 67, no. 1 (May 1985): 13–17. http://dx.doi.org/10.1002/ajpa.1330670104.

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Kumar, Kannan Anil, and Matthew B. Parkinson. "Reweighting anthropometric data using a nearest neighbour approach." Ergonomics 61, no. 7 (February 20, 2018): 923–32. http://dx.doi.org/10.1080/00140139.2017.1421265.

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Brolin, Erik, Dan Högberg, Lars Hanson, and Roland Örtengren. "Adaptive regression model for prediction of anthropometric data." International Journal of Human Factors Modelling and Simulation 5, no. 4 (2017): 285. http://dx.doi.org/10.1504/ijhfms.2017.087002.

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Brolin, Erik, Dan Högberg, Roland Örtengren, and Lars Hanson. "Adaptive regression model for prediction of anthropometric data." International Journal of Human Factors Modelling and Simulation 5, no. 4 (2017): 285. http://dx.doi.org/10.1504/ijhfms.2017.10008080.

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Gerver, W. J. M., and R. de Bruin. "Body composition in children based on anthropometric data." European Journal of Pediatrics 155, no. 10 (October 1996): 870–76. http://dx.doi.org/10.1007/bf02282836.

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Park, Jinwoo, Yunja Nam, Eunkyung Lee, and Sunmi Park. "Error detection in three-dimensional surface anthropometric data." International Journal of Industrial Ergonomics 39, no. 1 (January 2009): 277–82. http://dx.doi.org/10.1016/j.ergon.2008.05.009.

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Ben Azouz, Zouhour, Marc Rioux, Chang Shu, and Richard Lepage. "Characterizing human shape variation using 3D anthropometric data." Visual Computer 22, no. 5 (April 21, 2006): 302–14. http://dx.doi.org/10.1007/s00371-006-0006-6.

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Olmedo, M., R. Garcia, M. Barrientos, P. Chisholm, and J. Millan. "Correlation between estimated cardiovascular risk and anthropometric data." Atherosclerosis 235, no. 2 (August 2014): e212-e213. http://dx.doi.org/10.1016/j.atherosclerosis.2014.05.629.

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Khadem, Mohammad M., and Md Anisul Islam. "Development of anthropometric data for Bangladeshi male population." International Journal of Industrial Ergonomics 44, no. 3 (May 2014): 407–12. http://dx.doi.org/10.1016/j.ergon.2014.01.007.

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Brolin, Erik, Dan Högberg, Lars Hanson, and Roland Örtengren. "Adaptive regression model for synthesizing anthropometric population data." International Journal of Industrial Ergonomics 59 (May 2017): 46–53. http://dx.doi.org/10.1016/j.ergon.2017.03.008.

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den Bakker, Emil, Reinoud Gemke, Joanna A. E. van Wijk, Isabelle Hubeek, Birgit Stoffel-Wagner, Anders Grubb, and Arend Bökenkamp. "Accurate eGFR reporting for children without anthropometric data." Clinica Chimica Acta 474 (November 2017): 38–43. http://dx.doi.org/10.1016/j.cca.2017.09.004.

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Yahya, Mohd Shahir, Thiyagu Palaniandy, Noor Yasmin Zainun, and Musli Mohammad. "Development of Malaysian Primary School Children Anthropometrics Data for Designing School Furniture Parameters." Applied Mechanics and Materials 465-466 (December 2013): 1191–95. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1191.

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This paper explains the measurement of anthropometricdimensions ofprimary school children and investigation ofrisk factors associated with musculoskeletal disorders (MSDs) among primary school children. A total of 266 students within three different schools were participated in anthropometric data measurements and questionnaire survey of the MSDs prevelance complain among them. Ten anthropometric measurements (Stature, Sitting height, Sitting shoulder height, Popliteal height, Hip breadth, Elbow seat height, Buttock-popliteal length, Buttock-knee length, Thigh clearance and Weight) were used. Martin type anthropometer set, height scale and weighing scale were used as a direct measurement method for the data collection on this study. Musculoskeletal symptoms were recorded using Modified Nordic Body Map Questionnaires. In addition Rapid Upper Limb Assessment (RULA) was used to assess the awkward posture of the school children for both designs. The results of the proposed furniture shows a better RULA final score for each group of muscles which give a score ranging from only 1 to 2 (Acceptable Posture) compared to the existing furniture that need further investigation. This anthropometrics data is very useful to the furniture manufacturer in designing school furniture in order to reduce the mismatch between furniture designed and Malaysian primary school children.

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Nurzaime Zulaily, Aryati Ahmad, Mohd Razif Shahril, Fadzli Syed Abdullah, and Amran Ahmed. "RELIABILITY OF ANTHROPOMETRIC MEASUREMENTS CONDUCTED IN NATIONAL PHYSICAL FITNESS STANDARD (SEGAK) ASSESSMENTS AMONG SCHOOL-AGED ADOLESCENTS IN TERENGGANU, MALAYSIA." Malaysian Journal of Public Health Medicine 19, no. 2 (August 31, 2019): 141–48. http://dx.doi.org/10.37268/mjphm/vol.19/no.2/art.198.

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School-based health programs implemented by the Malaysian Ministry of Education (MOE) through the National Physical Fitness Standard (SEGAK) assessments provided an important platform in health status monitoring among schoolchildren. However, to date, there is still no reliability study conducted on this method. Therefore, this study aimed to determine the reliability of the anthropometric data collected by physical education (PE) teachers in the SEGAK assessments. Anthropometry measurements of standard six school adolescents involved in the Health of Adolescents in Terengganu study were taken by trained researchers using a standardised protocol. The anthropometrics data were then compared with PE teachers’ measurements from the SEGAK assessments obtained from the specifically developed Health Monitoring System database. Reliability of the anthropometric measurements were analysed using Pearson’s correlation test, Intraclass Correlation Coefficients (ICC), Bland-Altman plot and Cohen’s Kappa statistics. Intraclass correlation coefficient between teacher-measured and researcher-measured values shows good correlation in weight (ICC = 0.93), height (ICC = 0.98) and BMI (ICC = 0.91). The Bland-Altman plot showed a relatively small difference in mean of weight, height, and BMI between teacher-measured and researcher-measured value. The mean difference between teacher-measured and researcher-measured value of weight, height, and BMI were 1.8kg, 0.1cm, and 0.8kg/m2 respectively. Overall, Cohen’s Kappa statistics showed substantial agreement (κ = 0.642) in BMI categorisation between the two measurements. Findings from reliability analysis conducted affirmed that anthropometrics assessments conducted by PE teachers in SEGAK assessments are reliable to be used for identification of body weight status among school children and adolescents particularly in Terengganu, Malaysia.

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Moczynski, Allison N., Charles A. Weisenbach, and James S. McGhee. "Retrospective Assessment of U.S. Army Aviator Anthropometric Screening Process." Aerospace Medicine and Human Performance 91, no. 9 (September 1, 2020): 725–31. http://dx.doi.org/10.3357/amhp.5462.2020.

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INTRODUCTION: The current U.S. Army aviator anthropometric screening process for rotary-wing cockpit compatibility was codified over 30 yr ago. Critical to the process are the anthropometric standards that define what is acceptable for U.S. Army flight school applicants. The purpose of this study was to assess and optimize the efficiency of the standards in screening for anthropometric cockpit compatibility while maintaining safety.METHODS: A retrospective analysis was performed. Anthropometry and disposition data of flight school applicants from 2005 to 2014 were taken from the Aeromedical Electronic Resource Office database to determine efficiency of the process. Data on mishaps from 1972 to 2017 were retrieved from the Risk Management Information System database to determine the safety benchmark of the existing process, to which adjusted standards would be held. Adjustments to standards were modeled that would more efficiently pass applicants over the period studied without exceeding the established acceptable safety level.RESULTS: There were 40,136 (98.28%) applicants who passed the standards, while 702 (1.72%) failed. Most (98.52%) applicants who failed the standards and applied for an anthropometry exception to policy (ETP) received one. The models would pass up to 396 (99.25%) applicants who received ETPs without exceeding the established number of mishaps attributable to the anthropometry standards, which was found to be zero.DISCUSSION: The screening process is efficient and effective, but could be improved. Adjusting the standards could increase process efficiency by passing more applicants during their flight physical and widening the applicant pool, while maintaining the current level of safety.Moczynski AN, Weisenbach CA, McGhee JS. Retrospective assessment of U.S. Army aviator anthropometric screening process. Aerosp Med Hum Perform. 2020; 91(9):725731.

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de Onis, Mercedes, and Jean-Pierre Habicht. "Anthropometric Reference Data for International Use: Recommendations from a who Expert Committee." Food and Nutrition Bulletin 18, no. 2 (January 1997): 1–12. http://dx.doi.org/10.1177/156482659701800204.

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The World Health Organization (WHO) convened an Expert Committee to re-evaluate the use of anthropometry at different ages for assessing health, nutrition, and social well-being. The Committee's task included identifying reference data for anthropometric indices when appropriate, and providing guidelines on how the data should be used. For foetal growth, the Committee recommended an existing sex-specific multiracial reference. In view of the significant technical drawbacks of the current National Center for Health Statistics (NCHS)/WHO reference and its inadequacy for assessing the growth of breastfed infants, the Committee recommended the development of a new reference concerning weight and length/height for infants and children, which will be a complex and costly undertaking. Proper interpretation of mid-upper-arm circumference for pre-schoolers requires age-specific reference data. To evaluate adolescent height-for-age, the Committee recommended the current NCHS/WHO reference. Use of the NCHS body mass index (BMI) data, with their upper percentile elevations and skewness, is undesirable for setting health goals; however, these data were provisionally recommended for defining obesity based on a combination of elevated BMI and high subcutaneous fat. The NCHS values were provisionally recommended as reference data for subscapular and triceps skinfold thicknesses. Guidelines were also provided for adjusting adolescent anthropometric comparisons for maturational status. Currently, there is no need for adult reference data for BMI; interpretation should be based on pragmatic BMI cut-offs. Finally, the Committee noted that few normative anthropometric data exist for the elderly, especially for those over 80 years of age. Proper definitions of health status, function, and biologic age remain to be developed for this group.

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Kaeuper, Margaret. "Craniofacial Measurements in a Home-Based Public Health Study." Practicing Anthropology 21, no. 1 (January 1, 1999): 40–43. http://dx.doi.org/10.17730/praa.21.1.bq22775wj36x347t.

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Colleagues in nursing were puzzled when I told them I would be trained by an anthropologist to take anthropometric craniofacial measurements. As it turned out my new colleagues in anthropology were also surprised to find how well my training in public health nursing facilitated the successful collection of data. Young children are notoriously difficult subjects for the anthropometrist, yet the procedures used were, in kind, no different from what nurses routinely ask of a child. Thus, my previous experience as a public health nurse specializing in maternal child health allowed me to develop several useful strategies that resulted in successful collection of anthropometric data from over 1300 infants, with follow-up of some at one year and three years of age. As so often, cross-disciplinary approaches produce useful results, in this case the synthesis of an anthropometric methodology and a public health nurse's understanding of children and mothers.

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Leidman, Eva, Louise Masese Mwirigi, Lucy Maina-Gathigi, Anna Wamae, Andrew Amina Imbwaga, and Oleg O. Bilukha. "Assessment of Anthropometric Data Following Investments to Ensure Quality: Kenya Demographic Health Surveys Case Study, 2008 to 2009 and 2014." Food and Nutrition Bulletin 39, no. 3 (July 23, 2018): 406–19. http://dx.doi.org/10.1177/0379572118783181.

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Background: Evidence-based nutrition programs depend on accurate estimates of malnutrition derived from data collected in population representative surveys. The feasibility of obtaining accurate anthropometric data as part of national, multisectoral surveys has been a debated issue. Objectives: The study aimed to evaluate changes in anthropometric data quality corresponding to investments by the Kenya Ministry of Health and nutrition sector partners for the 2014 Kenya Demographic Health Survey. Methods: Anthropometric data collected during the 2008 to 2009 and 2014 Kenya surveys were reanalyzed to assess standard parameters of quality: standard deviation, skewness, and kurtosis of z-score values for 3 anthropometric indicators (weight for height, height for age, and weight for age), percentage of children with missing measurements and outlier values, digit preference, and heaping of age. Results: A total of 9936 households were selected in 2008 to 2009, and 39 679 households were selected in 2014. Standard deviation of z-scores for all 3 indicators was smaller in 2014 than in 2008 to 2009. Applying original Demographic and Health Survey exclusion criteria, weight for height z-scores were 1.16 in 2014, 10.1% narrower than 2008 to 2009. The percentage of outlying values declined significantly from 2008 to 2009 to 2014 for both height for age and weight for height ( P < .001). Digit preference scores in 2014 improved for both weight ( P = .011) and height ( P < .001) suggesting less rounding of terminal digits. Conclusions: All tests of data quality suggest an improvement in 2014 relative to 2008 to 2009, despite the complexity implied by the larger sample. This improvement corresponds with efforts to enhance training and supervision of anthropometry, suggesting a positive effect of these enhancements.

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Dianat, Iman, Mohammad Ali Karimi, Ahmad Asl Hashemi, and Samira Bahrampour. "Classroom furniture and anthropometric characteristics of Iranian high school students: Proposed dimensions based on anthropometric data." Applied Ergonomics 44, no. 1 (January 2013): 101–8. http://dx.doi.org/10.1016/j.apergo.2012.05.004.

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