Anthropometry in Design

Anthropometric data are often estimated using predictive formulas or standardized manikins.Most often, these approaches are intended as indicators of the ‘‘average’’ human. As such,its utility can be limited in that there is no individual who is truly average across multipledimensions, and relationships between measures may not be linear or the same betweenpeople. For example, a person with a 50th percentile arm length likely does not have a 50thpercentile leg length (it may be close or quite different). Further, many anthropometric tablesonly present average values (e.g., for center-of-mass location), making estimates of individualdifferences impossible.


A second limitation in the application of anthropometry arises from potential biases. As noted above, most of the larger datasets were derived several decades ago, thus not account2 Physical Ergonomic Analyses 767
ing for general and nontrivial secular trends toward larger body sizes across all populations. Many of these studies were also performed on military populations, and questions arise as to whether the values are representative in general. Additional biases can arise due to ethnic origins, age, and gender. Overall, application of anthropometric data requires careful attention to minimize such sources of bias.

Three traditional approaches have been employed when using anthropometry in design. Each may have value, depending on the circumstances, and differ in their emphasis on a portion of a population. The first, and most straightforward, is design for extremes. In this approach, one ‘‘tail’’ of the distribution in a measure is the focus. In the example above for door height, the tall males were of interest, since if those individuals are accommodated, then all shorter males and nearly all females will as well. Alternatively, the smaller individual
may be of interest, as when specifying locations where reaching is required: If the smallest individual can reach it, so will the larger ones.

The second approach, design for average, focuses on the middle of the distribution. This has also been termed the ‘‘min–max’’ strategy, as it addresses the minimal dimension needed for small individuals and the maximal dimensions for large individuals. A typical nonadjustable seat or workstation is an example of designing for the average. In this case, both the smallest and largest users may not be accommodated (e.g., unable to find a comfortable posture).

Design for adjustability is the third approach, and this seeks to accommodate the largest possible proportion of individuals. For example, an office chair may be adjustable in height and/or several other dimensions. While this approach is generally considered the best among the three, with increasing levels or dimensions of adjustability comes increasing costs. In practice, designers must balance these costs with those resulting from failure to accommodate some users.

In all cases, the design strategy usually involves a goal or criterion for accommodation. Where the large individual is of concern (e.g., for clearance), it is common practice to design for the 95th percentile males. Similarly, the 5th percentile female is used when the smaller individual is of concern (e.g., for reaching). When the costs of failure to accommodate individuals is high, the tails are typically extended. From the earlier example, it might be desirable to ensure that 99.99% (or more) of the population can fit through a doorway.
Application of anthropometry in the design process usually involves a number of steps.








Key anthropometric attributes need to first be identified, then appropriate sources of population data (or collect this if unavailable). Targets for accommodation are usually defined early (e.g., 99%) but may change as costs dictate. Mock-ups and/or prototypes are often built, which allow for estimating whether allowances are needed (e.g., for shoe height or gait in the doorway example). Testing may then be conducted, specifically with extremes of the population, to determine whether accommodations meet the targets.


Maury A. Nussbaum
Industrial and Systems Engineering
Virginia Polytechnic Institute and State University
Blacksburg, Virginia
Jaap H. van Diee¨n
Faculty of Human Movement Sciences
Vrije Universiteit
Amsterdam, The Netherlands

Mechanical Engineers’ Handbook: Materials and Mechanical Design, Volume 1, Third Edition.
Edited by Myer Kutz
Copyright  2006 by John Wiley & Sons, Inc.








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  • HOT CHAMBER DIE CASTING

    In this process, the diluent metal furnace together with cylinder injection molding machine and submerged in liquid metal. Injection cylinder pneumatic or hydraulic driven. In general, these types of Die Casting cicik only to deng, white tin, lead and alloys.
    On this machine has a main component: the cylinder plunger, neck geese (goose neck) and the nozzle.
    The liquid metal is pressed into the mold cavity pressure is maintained forever freezing occurs. Goose neck that liquid metal submerged position when the plunger on top. Then, molten metal is injected into the mold cavity very quickly


    COLD CHAMBER DIE CASTING
    In this machine, stove apart from the engine. Machine requires greater pressure to close the mold and filling the mold cavity.
    The way these machines work, starting from liquid metal melting and then poured into the plunger is adjacent to the mold, there was a hydraulic presses. This process is generally suitable for metals that have high melting temperatures, such as aluminum and magnesium.










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  • Anthropometric Data and Use

    Several large-scale anthropometric studies were conducted in the 1960s and 1970s, mostly in industrialized countries. Contemporary studies are typically of smaller scope, with the exception of the ongoing CAESAR (Civilian American and European Surface Anthropometry Resource) project (http: / / store.sae.org/ caesar / ). Anthropometric data are generally presented in tabular form, with some combination of means, standard deviations, and population percentiles. A normal statistical distribution is usually assumed, a simplification which is reasonable in most cases, though which also leads to larger magnitudes of errors at extremes
    of populations (e.g., the largest and smallest individuals).

    Standard statistical methods can be employed directly for a number of applications. If, for example, we wish to design the height of a doorway to allow 99% of males to pass through unimpeded, we can estimate this height from the mean ( ) and standard deviation ( ) as follows (again assuming a normal distribution). Male stature has, roughly, 175.58 and 6.68 cm. The standard normal variate, z, is then used along with a table of cumulative normal probabilities to obtain the desired value:









    where zA is the z value corresponding to a cumulative area A and Y is the value to be estimated. Here, z0.99 2.326, and thus Y 191.1 cm, or the height of a 99th percentile male. Clearly, however, further consideration is needed to address a number of practical issues. These include the relevance of the tabular values, whether this static value is applicable to a functional situation, and if /how allowances should be made for clothing, gait, etc.



    Percentile calculations, as given above, are straightforward only for single measures. With multiple dimensions, such as several contiguous body segments, the associated procedures become more involved. To combine anthropometric measures, it is necessary to create a new distribution for the combination. In general, means add, but variances (or standard deviations) do not. Equations are given below for two measures, X and Y (a statistics source should be consulted for methods appropriate for n 2 values):









    where indicates addition if measures are to be added and subtraction otherwise, cov is the covariance, and r is the correlation coefficient. As can be seen from these equations, the variance ( 2) of the combined  measure reduces to the sum of the individual variances when the two measures are independent, or cov(X,Y) rXY 0. Human measures are generally moderately correlated, however, with r on the order of 0.2–0.8 depending on the specific measures.





    Maury A. Nussbaum
    Industrial and Systems Engineering
    Virginia Polytechnic Institute and State University
    Blacksburg, Virginia
    Jaap H. van Diee¨n
    Faculty of Human Movement Sciences
    Vrije Universiteit
    Amsterdam, The Netherlands

    Mechanical Engineers’ Handbook: Materials and Mechanical Design, Volume 1, Third Edition.
    Edited by Myer Kutz
    Copyright  2006 by John Wiley & Sons, Inc.






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  • Anthropometry

    Fundamentals of Anthropometry and Measurement Anthropometry is the science that addresses the measurement and/or characterization of the human body, either individually or for populations. Engineering anthropometry is more application oriented, specifically incorporating human measures in design Examples include placement of a control so that most individuals can reach it, grip sizing for a hand-held tool, and height of a conveyor. Within ergonomics, anthropometric measures can be classified as either static or functional. The former are fundamental and generally fixed measures, such as the length of an arm or a body segment moment of inertia. Such static data are widely available from public and commercial sources. Functional measures are obtained during performance of some task or activity and may thus depend on several individual factors (e.g., training, experience, motivation). These latter measures are specific to the measurement situation and are hence relatively limited. Despite the availability of static measures, it is the functional measures that are directly relevant in design. The remainder of this section provides an overview of applied anthropometric methods. Results from anthropometry will also be critical in subsequent sections that address mechanical loading during task performance.

    Static anthropometric measures are of four types: linear dimensions (e.g., body segment lengths), masses or weights, mass center locations, and moments of inertia. Linear dimensions can be obtained quite simply using tape measures or calipers, with more advanced recent approaches using three-dimensional (3D) laser scanning. A key issue with respect to linear dimensions is the differentiation between surface landmarks and underlying joint centers of rotation. The former are easily located (e.g., the lateral and medial boney ‘‘knobs’’ above the ankle joint), and methods have been developed to translate these to estimates of underlying joint centers that are required for biomechanical modeling (Section 2.4).



    Mass (and/or volume) measures are often obtained using liquid immersion, though as
    noted above, recent scanning methods are also being employed. Locations of segment (or whole-body) center of mass can also be obtained using liquid immersion and a number of segmental balance methods. Segment moments of inertia are usually obtained using dynamical tests, where oscillatory frequencies are obtained during natural swinging or following a quick release. Representative geometric solids (e.g., a truncated cone) can also be used to model body parts and obtain analytical estimates.




    Maury A. Nussbaum
    Industrial and Systems Engineering
    Virginia Polytechnic Institute and State University
    Blacksburg, Virginia
    Jaap H. van Diee¨n
    Faculty of Human Movement Sciences
    Vrije Universiteit
    Amsterdam, The Netherlands

    Mechanical Engineers’ Handbook: Materials and Mechanical Design, Volume 1, Third Edition.
    Edited by Myer Kutz
    Copyright  2006 by John Wiley & Sons, Inc.



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