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CHAPTER 11ANTHROPOMETRY FOR PRODUCT DESIGN

Kathleen M. RobinetteAir Force Research LaboratoryWright-Patterson Air Force Base, Ohio

1 INTRODUCTION 330

2 ANTHROPOMETRY CASES:ALTERNATIVES, PITFALLS, AND RISKS 330

2.1 Averages and Percentiles 330

2.2 Alternative Methods 337

3 FIT MAPPING 342

4 THREE-DIMENSIONAL ANTHROPOMETRY 344

4.1 Why Three-Dimensional Scans? 344

5 SUMMARY 345

REFERENCES 346

1 INTRODUCTION

Did you know that designing for the 5th percentilefemale to the 95th percentile male can lead to poor andunsafe designs? If not, you are not alone. These andsimilar percentile cases, such as the 99th percentile male,are the only cases presented as anthropometry solutionsto many engineers and ergonomics professionals. In thischapter we review this and other anthropometry issuesand present an overview of practical effective methodsfor incorporating the human body in design.

Anthropometry , the study of human body measure-ment, is used in engineering to ensure the maximumbenefit and capability of products that people use. Theuse of anthropometric data in the early concept stage canminimize the size and shape changes needed later, whenmodifications can be very expensive. To use anthropom-etry knowledge effectively, it is also important to haveknowledge of the relationships between the body andthe items worn or used. The study of these relation-ships is called fit mapping . Databases containing bothanthropometry and fit-mapping data can be used as alessons-learned source for development of new prod-ucts. Therefore, anthropometry and fit mapping can bethought of as an information core around which productsare designed, as illustrated in Figure 1.

A case is a representation of a combination of bodymeasurements, such as a list of measurements on onesubject, the average measurements from a sample, athree-dimensional scan of a person, or a two- or three-dimensional human model. If the relationship betweenthe anthropometry and the fit of a product is simple orknown, cases may be all that is needed to arrive at aneffective design. However, if the relationship betweenthe anthropometry and the fit is complex or unknown,cases alone may not suffice. In these situations, fitmapping with a prototype, mock-up, or similar productis needed to determine how to accommodate the casesand to predict accommodation for any case.

The chapter is divided into three sections. Section 2deals with the selection of cases for characterizing

anthropometric variability. Section 3 covers fit-mappingmethods. Section 4 is devoted to some of the benefitsof the newest method in anthropometric data collection,three-dimensional anthropometry.

2 ANTHROPOMETRY CASES:ALTERNATIVES, PITFALLS, AND RISKS

When a designer or engineer asks the question “Whatanthropometry should I use in the design?” he or she isessentially asking, “What cases should I design around?”Of course, the person would always like to be given onecase or one list of measurements and be told that nothingelse is needed. That would make it simple. However, ifthe question has to be asked, the design is probably morecomplicated than that, and the answer is correspondinglymore complicated as well. In this section we discussalternative ways to determine which cases to use.

2.1 Averages and Percentiles

Since as early as 1952, when Daniels (1952) presentedthe argument that no one is average, we have known thatanthropometric averages are not acceptable for manyapplications. For example, an average human head isnot appropriate to use for helmet sizing, and an averagefemale shape is not appropriate for sizing apparel. Inaddition, we have known since Searle and Haslegrave(1969) presented their debate with Ed Hertzberg that the5th and 95th percentile people are no better. Robinetteand McConville (1982) demonstrated that it is not evenpossible to construct a 5th or 95th percentile humanfigure: The values do not add up. This means that 5thor 95th percentile values can produce very unrealisticfigures that do not have the desired 5th or 95th percentilesize for some of their dimensions.

The impact of using percentiles can be huge. Forexample, for one candidate aircraft for the T-1 program,the use of the 1st percentile female and 99th percentilemale resulted in an aircraft that 90% of females, 80%of African-American males, and 30% of white males

330 Handbook of Human Factors and Ergonomics, Fourth Edition Gavriel SalvendyCopyright © 2012 John Wiley & Sons, Inc.

ANTHROPOMETRY FOR PRODUCT DESIGN 331

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Figure 1 Anthropometric information as a design core.

could not fly. The problem is illustrated in Figure 2. Thepilots needed to be able, simultaneously, to see over thenose of the plane and operate the yoke, a control thatis similar to a steering wheel in a car. For the 99thpercentile seated eye height, the seat would be adjustedall the way down to enable the pilot to see over thenose. For the 1st percentile seated eye height, the seatwould be adjusted all the way up. Since the design usedall 1st percentile values for the full-up seat position, itaccounted for only a 1st percentile or smaller femalethigh size when the seat was all the way up. As aresult, it did not accommodate most female pilots’ thighsize without having the yoke interference as picturedin Figure 2. For designs such as this, where there areconflicting or interacting measurements or requirements,percentiles will not be effective. Cases that havecombinations of small and large dimensions are needed.

To understand when to use and when not to use aver-ages and percentiles, it is important to understand whatthey are and what they are not. Figure 3 illustrates aver-age and percentile values for stature and weight. Samplefrequency distributions for these two measurements areshown for the female North American data from theCAESAR survey (Harrison and Robinette, 2002) in theform of histograms. The averages for 5th and 95th per-centiles are indicated. The frequency is the count of thenumber of times that a value or range of values occurs,and the vertical bars in Figure 3 indicate the number ofpeople who had a stature or weight of the size indicated.For example, the one vertical bar to the right of the 95thpercentile weight indicates that approximately 10 peo-ple have a weight between 103 and 105 kg. Percentilesindicate the location of a particular cumulative fre-quency. For example, the 50th percentile is the point atwhich 50% have a smaller value, and the 95th percentileis the point at which 95% have a smaller value.

Figure 2 Problem that occurred when using 1st percen-tile female and 99th percentile male.

The average is a value for one measurement that fallsnear the middle of the distribution for that measurement.In this case the arithmetic average is shown. Anotherkind of central value is the 50th percentile, which will be

332 HUMAN FACTORS FUNDAMENTALS

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Figure 3 Stature and weight univariate frequencies, CAESAR, U.S. females.

the same as the arithmetic average when the frequencydistribution is symmetric. The stature distribution shownin Figure 3 is approximately symmetrical, so the averageand the 50th percentile differ by just 0.5%, whereas theweight distribution is not symmetric, so the average andthe 50th percentile differ by 5.4%.

2.1.1 Percentile IssuesPercentile values refer only to the location of thecumulative frequency of one measurement. This meansthat the 95th percentile weight has no relationship tothe 95th percentile stature. This is illustrated in Figure 4,which shows the two-dimensional frequency distributionfor stature and weight along with the one-dimensionalfrequency distributions that appeared in Figure 3. Staturevalues are represented by the vertical axis and weight bythe horizontal. The histogram from Figure 3 for statureis shown to the right of the plot, and the histogramfrom Figure 3 for weight is shown at the top of the

plot, each with its respective 5th and 95th percentilevalues. Each of the circular dots in the center of the plotindicates the location of one subject from the sample ofCAESAR U.S. females. The ellipse toward the centerthat surrounds many of the dots is the 90% ellipse; inother words, it encircles 90% of the subjects.

If a designer uses the “5th percentile female to the95th percentile female” approach, only two cases arebeing used. These two cases are indicated as blacksquares, one at the lower left and the other at the upperright of the two-dimensional plot in Figure 4. The oneat the lower left is the intersection of the 5th percentilestature and the 5th percentile weight. The one at theupper right is the intersection of the 95th percentilestature and the 95th percentile weight. The stature rangefrom the 5th to 95th percentile falls between the twohorizontal 5th and 95th percentile lines and containsapproximately 90% of the population. The weightrange from the 5th to 95th percentile falls between the


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Figure 4 Bivariate frequency distribution of stature and weight, CAESAR, U.S. females.

two vertical 5th and 95th percentile lines and containsapproximately 90% of the population. The people whofall between the 5th and 95th percentiles for bothstature and weight are only those people who fall withinthe intersection of the vertical and horizontal bands.The intersection contains only approximately 82%. If athird measurement is added, it makes the frequency dis-tribution three-dimensional, and the percentage accom-modated between the 5th and 95th percentiles for allthree measurements will be fewer still.

The bar in the weight histogram in Figure 3 that isjust to the right of the 95th percentile is the same as thebar that is just to the right of the 95th percentile weightin Figure 4. As stated above, approximately 10 subjectsfall at this point. If you look at the two-dimensional plot,you can see that these 10 subjects who are at approxi-mately the 95th percentile weight have stature valuesfrom as small as 1500 mm to as large as 1850 mm.In other words, women in this sample who have a95th percentile weight have a range of statures that

extends from below the stature 5th percentile to abovethe stature 95th percentile. This means that if the producthas conflicting requirements, the 5th to 95th percentilecases would not work effectively. For example, supposethat a zoo has an automatically adjusting platform foran exhibit that adjusts its height based on the person’sweight, and the exhibit designers want to make awindow or display large enough so that the populationcan see the exhibit. If this is designed for the 5thpercentile person to the 95th percentile person, theywould design for (1) the 5th percentile stature withthe 5th percentile weight as one case and (2) the 95thpercentile weight with the 95th percentile stature asthe other case. Let us use the same female populationdata to see what the 5th and 95th percentiles wouldaccommodate. At the 5th percentile case the platformwould be full up and the stature accommodated would be1525 mm. At the 95th percentile case the platform wouldbe full down and the stature accommodated would be1767 mm. This range of stature is 1767 – 1525 = 242


mm. This will accommodate the 5th percentile femaleto the 95th percentile female, but not the 5th percentilestature with an average weight or the 5th percentileweight with the average stature. The female who hasa 5th percentile weight of 49.2 kg but an average statureof 1639 mm would need 114 mm more headroom. Thefemale who has an average weight but a 5th percentilestature may not be able to see the display becausethe weight-adjusted platform is halfway down. This isillustrated in Figure 5.

2.1.2 When to Use Averages and PercentilesAverages, percentiles, and other one-dimensional sum-mary statistics such as the standard deviation, minimum,and maximum are very useful for comparing measure-ments captured in different ways or for comparing sam-ples from different populations to determine if thereare size and variability differences. For example, Krulet al (2010) provide a good example of the use ofsummary statistics for comparing self-reported valuesto measured values for stature, weight and body massindex. In Table 1, one-dimensional summary statisticsfrom the U.S. CAESAR sample (Harrison and Robi-nette, 2002) are compared with summary statistics fromthe U.S. ANSUR survey (Gordon et al., 1989) illus-trating another example of the proper use of summarystatistics. The CAESAR sample was taken from a civil-ian population, whereas the ANSUR sample was takenfrom a military population, the U.S. Army. The U.S.Army has fitness and weight limitations for its person-nel. As a result, the ANSUR sample has a more limited

range of variability for weight-related measurements.The effect of this can be seen by examining the dif-ferences in the weight and buttock–knee length ranges(minimum to maximum) versus the ranges for the othermeasurements that are less affected by weight.

You might also notice that the difference betweenCAESAR and ANSUR females in buttock–knee lengthis greater than the difference in CAESAR and ANSURmales in buttock–knee length. This highlights a keydifference between men and women. Women tend togain weight in their hips, buttocks, and thighs, whereasmen tend to gain weight or bulk in their waists andshoulders.

The ANSUR/U.S. CAESAR differences in Table 1are contrasted with another comparison of anthropomet-ric data in Table 2. This compares the U.S. CAESARdata with those collected in The Netherlands on theDutch population (TN). The Dutch claim to be the tallestpeople in Europe, and this is reflected in all the heightsand limb lengths. Both the male and female Dutch sub-jects are more than 30 mm taller on average than theirU.S. counterparts.

Averages and percentiles and other one-dimensionalstatistics can also be very useful for products that donot have conflicting requirements. In these instances theloss in accommodation with each additional dimensioncan be compensated for by increasing the percentilerange for each dimension. For example, if you want toensure 90% accommodation for a simple design problem(one that has no interactive measurements) with five keymeasurements, you can use the 1st and 99th percentile

5th Percentilestature, weight

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Figure 5 Woman with 5th percentile stature and average weight is not accommodated.


Table 1 Comparison of U.S. Civilian Summary Statistics (CAESAR Survey) with U.S. Army Statistics (ANSURSurvey)

N Mean Minimum Maximum Std. Dev.

Acromion Height, Sitting (mm)Females

CAESAR 1264 567.42 467.00 672.00 29.76ANSUR 2208 555.54 464.06 663.96 28.65

MalesCAESAR 1127 607.21 489.00 727.00 34.19ANSUR 1774 597.76 500.89 694.94 29.59

Buttock–Knee Length (mm)Females

CAESAR 1263 586.97 489.00 805.00 37.43ANSUR 2208 588.93 490.98 690.88 29.63


Sitting Eye Height (mm)Females

CAESAR 1263 755.34 625.00 878.00 34.29ANSUR 2208 738.71 640.08 864.11 33.24


Sitting Knee Height (mm)Females

CAESAR 1264 509.06 401.00 649.00 28.28ANSUR 2208 515.41 405.89 632.97 26.33


Sitting Height (mm)Females

CAESAR 1263 865.02 720.00 994.00 36.25ANSUR 2208 851.96 748.03 971.04 34.90


Stature (mm)Females

CAESAR 1264 1639.66 1248.00 1879.00 73.23ANSUR 2208 1629.38 1427.99 1869.95 63.61


Thumb Tip Reach (mm)Females

CAESAR 1264 738.65 603.30 888.00 39.55ANSUR 2208 734.61 605.03 897.89 36.45


Weight (kg)Females

CAESAR 1264 68.84 39.23 156.46 17.60ANSUR 2208 62.00 41.29 96.68 8.35



Table 2 Comparison of the U.S. (US) and Dutch (TN) Statistics from the CAESAR Survey

N Mean Minimum Maximum Std. Dev.

Acromion Height, Sitting (mm)Females

US 1264 567.42 467.00 672.00 29.76TN 687 589.53 490.98 709.93 33.20

MalesUS 1127 607.21 489.00 727.00 34.19TN 559 629.89 544.07 739.90 35.96

Buttock–Knee Length (mm)Females

US 1263 586.97 489.00 805.00 37.43TN 688 608.07 515.87 728.98 31.15

MalesUS 1127 618.93 433.00 761.00 35.93TN 558 636.22 393.95 766.06 37.44

Sitting Eye Height (mm)Females

US 1263 755.34 625.00 878.00 34.29TN 676 774.46 664.97 942.09 35.82

MalesUS 1127 808.07 681.00 995.00 39.24TN 593 825.35 736.09 957.07 39.71

Sitting Knee Height (mm)Females

US 1264 509.06 401.00 649.00 28.28TN 676 510.67 407.92 600.96 28.78

MalesUS 1127 562.35 464.00 671.00 31.27TN 549 557.09 369.06 680.97 35.97

Sitting Height (mm)Females

US 1263 865.02 720.00 994.00 36.25TN 687 884.64 766.06 1049.02 38.06

MalesUS 1127 925.77 791.00 1093.00 40.37TN 559 941.85 823.98 1105.92 42.54

Stature (mm)Females

US 1264 1639.66 1248.00 1879.00 73.23TN 679 1672.29 1436.12 1947.93 79.02

MalesUS 1127 1777.53 1497.00 2084.00 79.19TN 593 1808.08 1314.96 2182.88 92.81

Thumb Tip Reach (mm)Females

US 1264 738.65 603.30 888.00 39.55TN 690 751.22 632.97 889.25 37.71

MalesUS 1127 813.99 694.60 1027.00 44.11TN 564 826.7 488.70 1055.62 53.55

Weight (kg)Females

US 1264 68.84 39.23 156.46 17.60TN 690 73.91 37.31 143.23 15.81

MalesUS 1127 86.24 45.80 181.41 18.00TN 564 85.57 50.01 149.73 17.28


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Figure 6 Moving the percentile values out farther to get a desired joint accommodation of 90%.

values instead of the 5th and 95th. Each of the fivemeasurements restricts 2% of the population, so at mostyou would have 5 × 2% = 10% disaccommodated. Thisapproach is illustrated in Figure 6. The bars representedby the 5th percentile values would be moved to wherethe stars are in the figure.

To summarize, percentiles represent the proportionaccommodated for one dimension only. When used formore than one dimension, the combination of mea-surements will accommodate less than the proportionindicated by the percentiles. If the design has no con-flicting requirements, you can sometimes compensate bymoving out the percentiles. However, if the design hasconflicting requirements, using percentiles may accom-modate very few people, and an alternative set of casesis required.

2.2 Alternative MethodsThere are two categories of alternatives to percentiles:(1) use a sample of human subjects as fit models or(2) select a set of cases or representations of peoplewith relevant size and shape combinations. Generally,using a random sample with lots of subjects is notpractical, although new three-dimensional modeling andCAD technologies may soon change this. Therefore, theselection of a small number of cases that effectivelyrepresent the population is preferable. The first methodis typical practice in the apparel industry, but only

for one subject (a sample of one) for a central sizereferred to as the base size. They adjust for the restof the population using a process called grading. Thegrade is expressed as the increment of change from onesize to the next starting with the base size for a listof measurements. Therefore grading uses the secondmethod or what we refer to as cases. The success orfailure of the fit of the garment for the target populationis dependent upon the selection of the initial subject aswell as the selection of the cases, the grade increments.

2.2.1 Case SelectionThe purpose of using a small number of cases is tosimplify the problem by reducing to a minimum theamount of information needed. Generally, the first thingreduced is the number of dimensions. This is doneusing knowledge of the product and by examining thecorrelation of the dimensions that are related to theproduct. The goal is to keep just those that are criticaland have as little redundant information as possible.For example, eye height, sitting, and sitting heightare highly correlated; therefore, accommodating onecould accommodate the other, and only one would beneeded in case selection. The risk is that somethingimportant will be missed and therefore will not be wellaccommodated.

It is easiest if the number of critical dimensions canbe reduced to four or fewer, because all combinations


of small and large proportions need to be considered.If there are two critical dimensions, the minimumnumber of small and large combinations is four: small–small, small–large, large–small, and large–large.If there are three critical dimensions, the minimumnumber of small and large combinations is eight:small–large–large, small–small–large, small–large–small, small–small–small, large–small–small, large–small–large, large–large–small, and large–large–large.With more than four the problem gets quite complex,and a random sample may be easier to use.

The next simplification is a reduction in the combina-tions used. Often in a design, only the small or large sizeof a dimension is needed. For example, for a chair hipbreadth, sitting might be one of the critical dimensionsbut only the large size is needed to define the minimumwidth of the seat. If it does not have any interactive orconflicting effect with the other critical dimensions, itcan be used as a stand-alone single value. Also, if twoor more groups have overlapping cases, such as malesand females in some instances, it is possible to dropsome of the cases.

This process is best explained using an example ofa seated workstation with three critical dimensions: eyeheight, sitting; buttock–knee length; and hip breadth,sitting. The minimum seat width for this design shouldbe the largest hip breadth, sitting, but this is the onlyseat element that is affected by hip breadth, sitting,so it interferes with no other dimension. The desiredaccommodation overall is 90% of the male and femalepopulation. First the designer selected the hip breadth,sitting value by examining its summary statistics forthe large end of its distribution. These are shown inTable 3. As can be seen from the table, the women havea larger hip breadth, sitting than the men. Therefore,

Table 3 Hip Breadth, Sitting Statistics from U.S.CAESAR Survey (mm)

95th 99thN Mean Percentile Percentile Maximum

Females 1264 410 501 556 663Males 1127 376 435 483 635

the women’s maximum value will be used. If the 99thpercentile is used, approximately 1% of U.S. civilianwomen would be estimated to be larger. If 90% areaccommodated with the remaining two dimensions, only89% would be expected to be accommodated for allthree. It would be simplest to use the maximum andthen accommodate 90% in the other two. This was theapproach used by Zehner (1996) for the JPATS aircraft.An alternative is to assume some risk in the design andto select a smaller number than the maximum. This is ajudgment to be made by the manufacturer or customer.

Next we examine the two-dimensional (also calledbivariate) frequency distribution for eye height, sittingand buttock–knee length. The distribution for femalesubjects from the CAESAR database is shown inFigure 7, and the distribution for male subjects is shownin Figure 8. The stars in Figures 7 and 8 representthe location of the 5th and 95th percentiles, and theprobability ellipses enclose 90% of each sample. Toachieve the target 90% accommodation, cases that lieon the elliptical boundary are selected. Boundary caseschosen in this way represent extreme combinations ofthe two measurements. For example, in Figure 7, cases1 and 3 represent the two extremes for buttock–kneelength, and cases 2 and 4 represent the two extremesfor eye height, sitting. Note that the cases are moderate

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Figure 8 Bivariate frequency distribution for eye height, sitting and buttock–knee length for U.S. male sample (N =1125); 90% probability ellipse.

in size for one dimension but extreme for the other.The boundary ellipse provides combinations that are notcaptured in the range between small–small (5th/5th) orlarge–large (95th/95th) percentiles. Case selection ofthis type makes the assumption that if the boundarycases are accommodated by the design, so are all thosewithin the probability ellipse. Although this assumptionis valid for workspace design, where vision, reach, andclearance from obstruction are key issues, it is not nec-essarily true for design of clothing or other gear worn onthe body. In the latter application, an adequate number ofcases must be selected to represent the inner distributionof anthropometric combinations, which should be givenmuch more emphasis than the boundary cases.

The dimensions for the eight cases represented inFigures 7 and 8 are shown in Table 4. Note that thistable includes the same hip breadth, sitting for all cases.This is the hip breadth, sitting taken from Table 3and represents the smallest breadth that should be usedin the design. The dimensions for each case must be

Table 4 Case Dimensions for Seated WorkstationExample (mm)

Females Case 1 Case 2 Case 3 Case 4

Buttock–knee length 510 600 660 600Eye height, sitting 725 820 795 690Hip breadth, sitting 663 663 663 663

Males Case 5 Case 6 Case 7 Case 8

Buttock–knee length 541 655 690 595Eye height, sitting 760 890 855 725Hip breadth, sitting 663 663 663 663

applied to the design as a set. For example, the seatmust be adjustable to accommodate a buttock–kneelength of 510 mm at the same time that it is adjustedto accommodate an eye height, sitting of 725 mm and ahip breadth, sitting of 663 mm to accommodate case 1.

An option for reducing the number of cases is todrop those that are overlapping or redundant. If the riskis so small that differences in men and women will affectthe design significantly, it is possible to drop some ofthe overlapping cases and still accommodate the desiredproportion of the population. For example, male cases5 and 8 are not as extreme as female cases 1 and 4,and the accommodation risk due to dropping them issmall. The bivariate distribution in Figure 9 illustratesbuttock–knee length and eye height, sitting for both menand women. The final set of anthropometric cases isshown, as well as the location of the dropped cases,5 and 8.

2.2.2 Distributing CasesAs introduced in Section 2.2.1, all of the prior examplesmake the assumption that if the outer boundaries of thedistribution are accommodated, all of the people withinthe boundaries will also be accommodated. This is truefor both the univariate case approach (upper and lowerpercentile values) and the multivariate case approach(e.g., bivariate ellipse cases, as above). For products thatcome in sizes or with adjustments that are stepped ratherthan continuous, this may not be a valid assumption.Imagine a T-shirt that comes in only X-small and XX-large sizes. Few people would be accommodated. Forthese kinds of products, it is necessary to select, ordistribute, cases both at and within the boundaries.

For distributing cases it is important that there bemore cases than expected sizes or adjustment steps to


ensure that people are not missed between sizes orsteps. A good example of distributed cases is shown byHarrison et al. (2000) in their selection of cases for lasereye protection (LEP) spectacles. They used three keydimensions: face breadth for the spectacle width, nosedepth for the distance of the spectacle forward from theeye, and eye orbit height for the spectacle height. Theyused bivariate plots for each of these dimensions withthe other two and selected 30 cases to characterize thevariability for all three. They also took into account thedifferent ethnicities of subjects when selecting cases to

ensure adequate accommodation of all groups. One oftheir bivariate plots with the cases selected is shown inFigure 10.

For the LEP effort, the critical dimensions were usedto select individual subjects, and their three-dimensionalscans were used to characterize them as a case forimplementation in the spectacle design. Figure 11 illus-trates the side view of the three-dimensional scan forone of the cases. By using distributed cases throughoutthe critical dimension distribution, a broader range iscovered than using the equivalent number of subjects in

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Figure 9 Bivariate frequency distribution for eye height, sitting and buttock–knee length for U.S. male (N = 1127) andfemale (N = 1263) sample. Cases 5 and 8 were not included in the final set due to proximity to cases 1 and 4.

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Figure 11 Side view of one LEP case.

a random sample, and no assumption about the range ofaccommodation within one size is made. This permitsevaluation of the range of fit within a size and the degreeof size overlap during the design process.

2.2.3 Principal-Components Analysis

In the examples above, the set of dimensions wasreduced using judgment based on knowledge of theproblem and the relationship between measurements.Principal-components analysis (PCA) can be helpful inboth understanding the relationship between relevantmeasurements and reducing the set of dimensions toa small, manageable number. This technique has beenused effectively for aircraft cockpit crew station design(Bittner et al., 1987; Zehner et al., 1993; Zehner, 1996).

Human dimensions often have some relationshipwith each other. For example, sitting height and eyeheight, sitting are highly correlated. The relationshipsbetween a set of dimensions can be expressed aseither a correlation or a covariance matrix. PCA usesa correlation or covariance matrix and creates a newset of variables called components . The total numberof components is equal to the number of originalvariables, and the first component will always representthe greatest amount of variation in the distribution. Thesecond component describes the second greatest, andso on. An examination of the relative contributions, orcorrelations, of each original dimension and a particularcomponent can be used to interpret and “name” thecomponent. For example, the first component usuallydescribes overall body size and is defined by observinga general increase in the values for the originalanthropometric dimensions as the value, or score, of thefirst component increases.

The premise in using PCA for accommodation caseselection is that if most of the total variability in therelevant measurements can be represented in the first

two or three components these components can beused to reduce and simplify your case selection. Forexample, to write the anthropometric specifications forcockpit design in Joint Primary Air Training System(JPATS) aircraft, Zehner (1996) used the first andsecond components from a PCA on six cockpit-relevantanthropometric dimensions. The first two componentsexplained 90% of the total variability for all six com-bined measurements. This was approximately the samefor each gender (conducted in separate analyses). Zehnerthen used a 99.5% probability ellipse on the first twoprincipal components to select the initial boundarycases. One of the genders is shown in Figure 12. Com-bining the initial set of cases from both genders (withsome modification) resulted in a final set of JPATS casesthat offered an accommodation of 95% for the womenand 99.9% for the men. The first principal componentwas defined as size; the second was a contrast betweenlimb length and torso height (short limbs/tall torso vs.long limbs/short torso).

Unlike compiled percentile methods (or compiledbivariate approaches when there are more than two vari-ables), multivariate PCA takes into account the simulta-neous relationship of three or more variables. However,with PCA the interpretation of the components may notalways be clear, and it can be more difficult to under-stand what aspect of size is being accommodated. Analternative way to use PCA is to use it only to under-stand which dimensions are correlated with others andthen select the most important single dimensions to rep-resent the set as a key dimension. In this way, the keydimension is easier to understand.

The chief limitation of PCA is that all of the dimen-sions are accepted into the analysis as if they haveequal design value and PCA has no way to knowthe design value. As a result, accommodating the

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Figure 12 PCA bivariate and 99.5% boundary for thefirst and second principal components for one gender ofthe JPATS population. Initial cases 1–8 for this genderare regularly distributed around the boundary shape.


components will accommodate some of the variabilityof the less important dimensions at the expense of themore important ones. Also, PCA is affected by thenumber of correlated dimensions used of each type. Forexample, if 10 dimensions are used and 9 of them arestrongly correlated with one another, the one dimensionthat is not correlated with any other may end up beingcomponent 4. Since it is one of 10 dimensions, itrepresents 10% of the total variability. So it is possibleto accommodate 90% of the variability in the first threecomponents and not accommodate the most importantdimension. Therefore, when using PCA it is importantto (1) include only dimensions that are both relevant andimportant and (2) check the range of accommodationachieved in the cases for each individual dimension.

3 FIT MAPPING

Fit mapping is a type of design guidance study thatprovides information about who a product fits well andwho it does not. When anthropometry is used in productdesign without the knowledge of fit, many speculationsmust be made about how to place the anthropometry inthe design space and the range of accommodation. Asa result, even with digital human models and computer-aided design, it is often the case that the first prototypesdo not accommodate the full range of the population andmay accommodate body size regions that do not existin the population.

Fit mapping combines performance testing of pro-totypes or mock-ups with anthropometric measurementto “map” the fit effectiveness of a product for differ-ent body sizes and shapes. Fit effectiveness means thatthe desired population is accommodated without wastedsizes or wasted accommodation regions. Because mostperformance-based fit tests cannot be done on digitalmodels, fit mapping involves using human subjects todo the assessments. The following is a list of thingsneeded for a fit-mapping study:

1. Human subjects drawn to represent a broadrange of variability

2. A prototype or sample of the product (multiplesamples of each size is desirable)

3. A testable concept-of-fit definition4. An expert fit evaluator or one who is trained to

be consistent using the concept-of-fit definition5. Anthropometry measuring equipment6. Multivariate analysis software and knowledge7. Survey data from the target population with

relevant measurements

The study process consists of:

1. Scoring the fit for each size that the subject candon against the concept of fit

2. Measuring the subjects3. Analyzing the data to determine:

a. The key size-determining dimensionsb. The range of accommodation for each size

with respect to the key dimensions

c. General design or shaping issuesd. Size or shape gaps in target population

coveragee. Size or shape overlaps in target population

coverage

The end result of the study is that wasted sizesor adjustment ranges are dropped, sizes or adjustmentranges are added where there are gaps, and design andreshaping recommendations are provided to make theproduct fit better overall. One example of the magnitudeof the improvement that can be achieved with the useof fit mapping was demonstrated in the Navy women’suniform study (Mellian et al., 1990; Robinette et al.,1990). The Navy women’s uniform consisted of twojackets, two skirts, and two pairs of slacks. The fitmapping consisted of measuring body size and assessingthe fit of each of the garments on more than 1000 Navywomen. Prior to the study, the Navy had added odd-numbered sizes in an attempt to improve fit, because75% of all Navy recruits had to have major alterations.The sizes included sizes 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 18, 20, and 22, with three lengths for each, fora total of 42 sizes.

The results indicated three important facts. First,there was 100% overlap in some of the sizes. For eachof the items, sizes 7 and 8, 9 and 10, 11 and 12, 13and 14, and 15 and 16 fit the same subjects equallywell. Second, the size of best fit was different for nearlyevery garment, with some women wearing up to fourdifferent sizes. For example, one woman had the best fitin a size 8 for the blue skirt, size 10 for the white skirt,size 12 for the blue slacks, and size 14 for the whiteslacks. Third, most women did not get an acceptable fitin any size.

The size overlap was examined and it was deter-mined that the difference between the sizes was lessthan the manufacturing tolerance for a size, which was12 in. Therefore, the manufacturers had actually usedexactly the same pattern for sizes 7 and 8, 9 and 10,11 and 12, 13 and 14, and 15 and 16. Therefore, sizes7, 9, 11, 13, and 15 in all three of their lengths couldbe removed with no effect on accommodation.

The difference in which size fits a given body wasresolved by renaming the sizes for some of the garments,to make them consistent. This highlights the fact that thesize something is designed to be is not necessarily thesize it actually is. Fabric, style, concept of fit, function,and many other factors affect fit. Many of these cannotbe known without fit testing on human subjects.

Finally, the women who did not get an acceptable fitin any size were proportioned differently than the sizerange. They had either a larger hip for the same waist ora smaller hip for the same waist as the Navy size range.This is an example of an interaction or conflict in thedimensions. All of the sizes were in a line consisting ofthe same shape scaled up and down. This is consistentwith common apparel sizing practice. Most apparelcompanies start with a base size, such as a 10 or a 12,and scale it up and down along a line. The scaling iscalled grading . This is illustrated in Figure 13. Thegrading line is shown in bold in Figure 13. The sizes


that fall along this line are similar to those used in theNavy women’s uniform. Note the overlapping of theodd-numbered sizes with the even-numbered sizes inone area. This is the area where there were more sizesthan necessary. Also, note that above and below thegrading line, no sizes are available.

Figure 14 illustrates the types of changes made to thesizing to make it more effective. (Note that the sample ofwomen shown is that of the civilian CAESAR survey,not Navy women, who do not have the larger waistsizes.) The overlapping sizes have been dropped. Thesizes shown above the grade line have a larger hip for

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Figure 13 Before fit mapping apparel sizes.

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Figure 14 After fit-mapping sizes.


the same waist and are called plus hip (+), and thesizes below the line have a smaller hip for the samewaist and are called minus hip (–). Before these sizeswere added, women who fell in the plus-hip region hadto wear a size with a very large waist in order to fittheir hip. Then they had to have the entire waist-to-hipregion altered. Women who fell in the minus-hip regionpreviously had to get a garment that was way too largein the hips in order to get a fit for their waist. Addingsizes with the modified hip-to-waist proportion resultedin accommodating 99% of the women without needingalternations. The end result of adjusting the sizing basedon fit mapping was to improve accommodation from 25to 99%, with the same number of sizes (Figure 14).

4 THREE-DIMENSIONAL ANTHROPOMETRY

Three-dimensional anthropometry has been around sincethe advent of stereophotography. Originally stereopairshad to be viewed through a stereoviewer and digitizedmanually, and it was very time consuming. This processis described by Herron (1972). However, digital photog-raphy allowed us to automate the process, and this hasdramatically affected our ability to design effectively.Automated three-dimensional scanning began to take offin the 1980s (Robinette, 1986). Now there are manytools available to use and analyze three-dimensionalscan data, and the first civilian survey to provide whole-body scans of all subjects, CAESAR, was completed in2002 (Blackwell et al., 2002; Harrison and Robinette,2002; Robinette et al., 2002). We describe briefly heresome of the benefits of the new technology.

4.1 Why Three-Dimensional Scans?

By far the biggest advantage of three-dimensional sur-face anthropometry is visualization of cases, particularly

Male with 99th percentilehip breadth

Two subjects overlaid

Female with 99th percentilehip breadth

Male hip breadth

Female hip breadth

Figure 15 View of male and female with 99th percentilehip breadth, sitting.

the ability to visualize them with respect to the equip-ment or apparel they wear or use. When cases areselected, some assumptions are made about the measure-ments that are critical for the design. Three-dimensionalscans of the subjects often reveal other important infor-mation that might otherwise have been overlooked. Anexample of this is illustrated in Figure 15. When design-ing airplane, stadium, or theater seats, two commonassumptions are made: (1) that the minimum width ofthe seat should be based on hip breadth, sitting and (2)that the minimum width of the seat should be basedon the large male. In Figure 15 we see the scans oftwo figures overlaid, a male with a 99th percentile hipbreadth, sitting and a female with a 99th percentile hipbreadth, sitting. The male figure is in dark gray and thefemale in light gray. It is immediately apparent that thefemale figure has broader hips than the male. Althoughshe is shorter and has smaller shoulders, her hips arewider by more than 75 mm (almost 3 in.). Second, itis also clear that the shoulders and arms of the malefigure extend out beyond the female hips. The breadthacross the arms when seated comfortably is clearly amore appropriate measure for the spacing of seats.

For the design of a vehicle interior, measures suchas buttock–knee length and eye height, sitting are oftenconsidered to be key. Figure 16 shows two women who

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Figure 16 Two women with the same buttock–kneelength and eye height, sitting.


have the same buttock–knee length and eye height,sitting. However, it is immediately clear from theimage that because of the difference in their soft tissuedistribution, they would have very different needsin terms of steering wheel placement. These thingsare much more difficult to comprehend by looking attables of numbers. Three-dimensional anthropometrycaptures some measurements, such as contour change,three-dimensional landmark locations, or soft tissuedistribution that cannot be captured adequately withtraditional anthropometry. Finally, three-dimensionalanthropometry offers the opportunity to measure thelocation of a person with respect to a product for usein identifying fit problems during fit mapping andeven for creating custom fit apparel or equipment. Forexample, by scanning subjects with and without a flighthelmet and examining the range of ear locations withinthe helmet, fit problems due to ear misplacement canbe identified. This is illustrated in Figure 17.

Figure 17 shows four examples of using three-dimensional anthropometric measurement to visualizeand quantify fit. This figure was created using scansof the subjects with and without the helmet andsuperimposing the two images in three dimensions usingsoftware called Integrate (Burnsides et al., 1996). Theimage at the upper left of Figure 17 shows the locationof the ears of eight subjects in the helmet being tested.The red curved lines show the point at which the subjectscomplained of ear pain. The image at the lower leftshows the locations of two different subjects in the samehelmet as they actually wore it to fly, demonstratingdifferent head orientations. The image at the upper rightshows the 90 and 95% accommodation ellipses for thepoint on the ear called the tragion for those subjects whodid not complain of ear pain. The image at the lowerright shows the spread of the tragion points for those

subjects who did not complain of ear pain along withone of the subject’s ears (subject 4). It can be seen thatthe points are not elliptical but seem to have a concaveshape, indicating a rotational difference between earlocations. These four images together with the fit andcomfort evaluations completed by the subjects enablean understanding of the geometry of ear fit in thathelmet. Without the three-dimensional images, the fitand comfort scores are difficult to interpret.

The new challenge is to combine static three-dimensional models with human motion. The entertain-ment industry has been combining these two technolo-gies, but their interest is in rapidly characterizing andsensationalizing the unreal rather than representing truth.Cheng and Robinette (2009) and Cheng et al. (2010)describe the challenge of characterizing true humanvariability dynamically and present some approaches toaddressing the challenge.

5 SUMMARY

Whether a product is personal gear (such as clothingor safety equipment), the crew station of a vehicle, orthe layout of an office workspace, accommodating thevariation in shape and size of the future user populationwill have an impact on a product’s ultimate success.This chapter describes and demonstrates the use ofcases, fit mapping, and three-dimensional anthropometryto design effectively, simultaneously minimizing costand maximizing accommodation. In the section oncases alternatives to the often misused percentiles arediscussed, including the use of PCA. The section onfit mapping explains how to incorporate knowledge ofthe relationship between the human and the product.The best anthropometric data in the world are not

Eight subjects' ears

Subject 4 earsTragion,all subjects

Size largeaxis system

Size largefit range

Size largeaxis system–2 subjects

Size x-large95% tragion ellipse

Size x-large90% tragion ellipse

Size x-largeaxis system

Figure 17 Three-dimensional scan visualizations to relate to fit-mapping data for ear fit within a helmet.


sufficient to create a good design if the relationshipbetween the anthropometry and the product proportionsthat accommodate it is not known. Fit mapping is thestudy of this relationship. The fit-mapping process isdescribed with examples to demonstrate its benefits.

Finally, for complex multidimensional design prob-lems, three-dimensional imaging technology provides anopportunity to visualize and contrast the variation in asample and to quantify the differences between locationsof a product on subjects who are accommodated versusthose who are not. The technology can also be usedto capture shape or morphometric data, such as contourchange, three-dimensional landmark locations, or softtissue distribution that cannot be captured adequatelywith traditional anthropometry. Therefore, three-dimen-sional anthropometry offers comprehension of accom-modation issues to a degree not possible previously.

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