Books: 'Anthropometric data'

1

HFES 300 Committee. Guidelines for using anthropometric data in product design. Santa Monica, Calif: Human Factors and Ergonomics Society, 2004.

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2

J, Kuczmarski Robert, and National Center for Health Statistics (U.S.), eds. Anthropometric data and prevalence of overweight for Hispanics, 1982-84. Hyattsville, Md: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Health Statistics, 1989.

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3

Najjar, Matthew F. Anthropometric reference data and prevalence of overweight, United States, 1976-80. Hyattsville, Md: U.S. Dept. of Health and Human Services, Public Health Service, National Center for Health Statistics, 1987.

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4

D, Fryar Cheryl, Ogden Cynthia L, National Center for Health Statistics (U.S.), and National Health and Nutrition Examination Survey (U.S.), eds. Anthropometric reference data for children and adults: United States, 1988-1994. Hyattsville, Md: U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2009.

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5

Kostermans, Kees. Assessing the quality of anthropometric data: Background and illustrated guidelines for survey managers. Washington, D.C: World Bank, 1994.

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6

Peebles, Laura. Adultdata: The handbook of adult anthropometric and strength measurements : data for design safety. London: Department of Trade and Industry, 1998.

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7

), National Center for Health Statistics (U S. Anthropometric reference data for children and adults: United States, 2007-2010 : data from the nathional health and nutrition survey. Hyattsville, Md: U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2012.

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8

Brainerd, Elizabeth. Reassessing the standard of living in the Soviet Union: An analysis using archival and anthropometric data. Bonn, Germany: IZA, 2006.

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9

Tassenaar, Vincent. Regional differences in standard of living in the Netherlands, 1800-1875: A study based on anthropometric data. Groningen, Netherlands: Groningen Growth and Development Centre (University of Groningen), 1995.

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10

Jürgens, Hans W. International data on anthropometry. Geneva: International Labour Office, 1990.

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11

Moore-Jansen, Peer H. Data collection procedures for forensic skeletal material. 3rd ed. Wichita, Kan: Wichita State University, Dept. of Anthropology, 1994.

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12

Moore-Jansen, Peer H. Data collection procedures for forensic skeletal material. 3rd ed. Knoxville, Tenn: Forensic Anthropology Center, Dept. of Anthropology, University of Tennessee, 1994.

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13

Zhongguo ren ti zhi diao cha: Survey of anatomical data and variations of Chinese. Shanghai: Shanghai ke xue ji shu chu ban she, 1985.

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14

Eston, Roger, and Thomas Reilly. Kinanthropometry and exercise physiology laboratory manual: Tests, procedures and data : Physiology. 3rd ed. London: Routledge, 2009.

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15

1951-, Wilson J. R., and Great Britain. Consumer Safety Unit., eds. Childata: The handbook of child measurements and capabilities : data for design safety. Nottingham: Consumer Safety Unit, Department of Trade and Industry, 1995.

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16

Das, S. R. Mixed-longitudinal growth data for 22 measures, the Sarsuna Barisha series (301 boys and 261 girls, 6 months to maturity), West Bengal, India. Calcutta: Anthropological Survey of India, Govt. of India, Dept. of Culture, 1985.

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17

Das, S. R. Mixed-longitudinal growth data for 22 measures, the Sarsuna Barisha series (301 boys and 261 girls, 6 months to maturity), West Bengal, India. Calcutta: Anthropological Survey of India, Govt. of India, Dept. of Culture, 1985.

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18

National Research Council (U.S.). Whither Biometrics Committee, ed. Biometric recognition: Challenges and opportunities. Washington, D.C: National Academies Press, 2010.

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19

Rathgeb, Christian. Iris Biometrics: From Segmentation to Template Security. New York, NY: Springer New York, 2013.

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20

Dikovics, Anne. Nutritional assessment: Case study methods. Philadelphia, PA: G.F. Stickley, 1987.

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21

Fitness, performance, and health norms. Champaign, IL: Human Kinetics, 2006.

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22

A, Ross Arun, Nandakumar Karthik, and SpringerLink (Online service), eds. Introduction to Biometrics. Boston, MA: Springer Science+Business Media, LLC, 2011.

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23

WHO child growth standards: Growth velocity based on weight, length and head circumference : methods and development. Geneva, Swtizerland: World Health Organization, Department of Nutrition for Health and Development, 2009.

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24

Mosk, Carl. Making health work: Human growth in modern Japan. Berkeley, Calif: University of California Press, 1996.

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25

K, Bhattacharyya S., and Anthropological Survey of India, eds. All India anthropometric survey.: Basic anthropometric data. Calcutta: Anthropological Survey of India, 1993.

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26

All India anthropometric survey, north zone: Basic anthropometric data. Calcutta: Anthropological Survey of India, 1988.

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27

J, Sreenath, Ahmad S. H, and Anthropological Survey of India, eds. All India anthropometric survey.: Analysis of data. [Calcutta]: Anthropological Survey of India, 1989.

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28

K, Rakshit Hirendra, and Anthropological Survey of India, eds. All India anthropometric survey, south zone: Basic data. Calcutta: Anthropological Survey of India, Ministry of Human Resource Development, Dept. of Culture, Govt. of India, 1988.

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29

Human Factors and Ergonomics Society. and HFES 300 Committee., eds. Guidelines for using anthropometric data in product design. Santa Monica, CA: Human Factors and Ergonomics Society, 2004.

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30

A, McDowell Margaret, and National Center for Health Statistics (U.S.), eds. Anthropometric reference data for children and adults, United States, 2003-2006. Hyattsville, MD: U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2008.

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31

Anthropometric and strength data of Indian agricultural workers for farm equipment design. Bhopal: Central Institute of Agricultural Engineering, 2009.

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32

Methodology for the Sizing and Design of Protective Helmets Using Three-Dimensional Anthropometric Data. Storming Media, 1998.

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33

Lyndon B. Johnson Space Center., ed. Anthropometric data from launch and entry suited test subjects for the design of a recumbent seating system. Houston, Texas: National Aeronautics and Space Administration, Lyndon B. Johnson Space Center, 1993.

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34

Lyndon B. Johnson Space Center., ed. Anthropometric data from launch and entry suited test subjects for the design of a recumbent seating system. Houston, Texas: National Aeronautics and Space Administration, Lyndon B. Johnson Space Center, 1993.

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35

Creadick, Anna. Disability’s Other. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190458997.003.0002.

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The notion of “disability” relies on the concept of “normal.” Like disability, normality has a traceable history as an epistemological category. The mobilization of soldiers during World War II and, to a lesser degree, World War I, meant thousands of minds and bodies could be, and were, measured. A curious obsession with defining “normal” took hold, as doctors, scientists, and anthropologists gathered and applied statistical data to try measure “normal” bodies and describe “normal” character. Enlistees were subjected to psychological testing; sexologists used anthropometric methods to map the “normal” American body; and an interdisciplinary team at Harvard launched a longitudinal study of “normal men.” Taken together, such pursuits of “normality” were inextricable from midcentury anxieties about mental health, embodiment, masculinity, and the nation. By illuminating and gendering the “normal,” such forces functioned both to evoke and then exclude “disabled” bodies from the social body.

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36

Barbara, Richardson, Cleaton-Jones Peter, Granath Lars, and South African Medical Research Council., eds. Dental caries, nutrient intake, dietary habits, anthropometric status, oral hygiene, and salivary factors and microbiota in South African black, Indian and white 4-5 year-old children: Descriptive data from a survey in 1984. Parow, South Africa: Medical Research Council, 1991.

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37

Claessens, Albrecht L., Gaston Beunen, and Robert M. Malina. Anthropometry, physique, body composition, and maturity. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0003.

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The appropriate normalization of exercise performance data for differences in body size underpins the clarification of growth and maturational influences on physiological function. Therefore, scaling is an issue of fundamental importance for all paediatric exercise scientists. The selection and application of a scaling method appropriate for the data and research question being addressed is at least as important as ensuring that the methodology used to collect the data is valid, reliable, and appropriate for use with young people. Several scaling methods are available and some methods can be applied in different ways. Unfortunately, taken as a whole, the extant literature presents a confusing picture as to which of these techniques is preferable, how they should be applied, and the meaning of the results obtained. The aim of this chapter is to clarify these issues through a description of the techniques available for analysing both cross-sectional and longitudinal data sets, highlighting their statistical and theoretical derivations.

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38

M, Robinette Kathleen, ed. CAESAR measurements on CD-ROM: Including 1-D data with 3-D landmarks. [Warrendale, Pa.]: Society of Automative Engineers, 2003.

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39

Robinette, Kathleen M. Caesar Measurements on CD-ROM: Including 1-D Data with 3-D Landmarks. SAE International, 2003.

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40

Cox, Mary Elisabeth. Hunger in War and Peace. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198820116.001.0001.

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What is the impact of war on non-combatants, particularly women and children? In this innovative analysis of nutritional deprivation among ordinary German citizens during the First World War, Mary Elisabeth Cox finds that the effects of the war and the Allied interdiction of food supplies—which became known in Germany as the ‘Hungerblockade’—resulted in diminished heights and weights of children far from the battlefield. During the war, Germany defiantly proclaimed that their country could not be starved out. In a military sense, this was likely to be the case, and many modern historians argue persuasively that Germany lost on the battlefield. Yet modern analyses of height and weight records for hundreds of thousands of school children reveal a grim truth: even if Germany did not lose the war because of food insecurity, the war blockade resulted in hunger for millions of German infants. Desperately struggling to feed their families under the growing spectre of starvation, many mothers chose to sacrifice their own well-being for the benefit of their families. National and local policies within Germany often exasperated food insecurity. Modern analysis of anthropometric data now brings into question both long-held assumptions about the divide between rural and urban health, and legal and moral arguments in support of the blockade. Combined with contemporary letters, diaries, and news reports, these data provide an expanded picture of the levels of health and nutritional deprivation across society. This story of one of the most vicious wars in history is not devoid of compassion. Following the eventual lifting of the British blockade, the victorious powers and nations throughout the world sent millions of tons of food into Germany, relief which is mirrored in drawings and letters of gratitude from hundreds of German school children, and which can be seen as a surge of growth in height and weight measurements.

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41

G, Eston Roger, and Reilly Thomas 1941-, eds. Kinanthropometry and exercise physiology laboratory manual: Tests, procedures and data. New York: E & FN Spon, 1996.

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42

Gurgens. International Data on Anthropometry: Occupational Safety and Health Series Report #65 (Labour-Management Relations Series,). International Labour Office, 1990.

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43

Gaiha, Raghav, Raghbendra Jha, Vani S. Kulkarni, and Nidhi Kaicker. Diets, Nutrition, and Poverty. Edited by Ronald J. Herring. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780195397772.013.029.

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This chapter addresses a persistent tension in current debates over food security, with illustrative data from India. The case allows us to disaggregate concepts in food policy that are often lumped together, so as to better understand what is at stake in rapidly changing economies more generally. Despite rising incomes, there has been sustained decline in per capita nutrient intake in India in recent years. The assertion by Deaton and Dreze (2009) that poverty and undernutrition are unrelated is critically examined. A demand-based model in which food prices and expenditure played significant roles proved robust, while allowing for lower calorie “requirements” due to less strenuous activity patterns, life-style changes, and improvements in the epidemiological environment. This analysis provides reasons for not delinking nutrition and poverty; it confirms the existence of poverty-nutrition traps in which undernutrition perpetuates poverty. A new measure of child undernutrition that allows for multiple anthropometric failures (e.g., wasting, underweight, and stunting) points to much higher levels of undernutrition than conventional ones. Dietary changes over time, and their nutritional implications, have welfare implications at both ends of the income and social-status pyramids. Since poverty is multidimensional, money-metric indicators such as minimum income or expenditure are not reliable, because these cannot adequately capture all the dimensions. The emergent shift of the disease burden toward predominately food-related noncommunicable diseases (NCDs) poses an additional challenge. Finally, the complexity of normative issues in food policy is explored. Current approaches to food security have veered toward a “right-to-food” approach. There are, however, considerable problems with creating appropriate mechanisms for effectuating that right; these are explored briefly. Cash transfers touted to avoid administrative costs and corruption involved in rural employment guarantee and targeted food-distribution programs are likely to be much less effective if the objective is to enable large segments of the rural population to break out of nutrition-poverty traps. The chapter ends by exploring an alternative model, based on the same normative principle: a “right to policies,” or a “right to a right.”

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44

Eston, Roger, and Thomas Reilly. Kinanthropometry and Exercise Physiology Laboratory Manual : Tests, Procedures and Data : Volume One : Anthropometry and Volume Two: Exercise Physiology. Taylor & Francis Group, 2020.

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45

Eston, Roger, and Thomas Reilly. Kinanthropometry and Exercise Physiology Laboratory Manual : Tests, Procedures and Data : Volume One : Anthropometry and Volume Two: Exercise Physiology. Taylor & Francis Group, 2020.

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46

Kinanthropometry and Exercise Physiology Laboratory Manual : Tests, Procedures and Data : Volume One : Anthropometry and Volume Two: Exercise Physiology. Taylor & Francis Group, 2020.

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47

Eston, Roger, and Thomas Reilly. Kinanthropometry and Exercise Physiology Laboratory Manual : Tests, Procedures and Data : Volume One : Anthropometry and Volume Two: Exercise Physiology. Taylor & Francis Group, 2020.

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48

J, Grother P., Casasent David Paul, and National Institute of Standards and Technology (U.S.), eds. Distortion-tolerant filter for elastic-distorted fingerprint matching. [Gaithersburg, Md.]: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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49

Distortion-tolerant filter for elastic-distorted fingerprint matching. [Gaithersburg, Md.]: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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50

Distortion-tolerant filter for elastic-distorted fingerprint matching. [Gaithersburg, Md.]: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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Books on the topic 'Anthropometric data'

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