Creating Virtual User Populations by Analysis of Anthropometric Data

Transcription

1 Creating Virtual User Populations by Analysis of Anthropometric Data Matthew B. Parkinson Engineering Design and Mechanical Engineering, Penn State University, University Park, PA 16802, USA Matthew P. Reed University of Michigan Transportation Research Institute, Ann Arbor, MI 48109, USA Abstract Anthropometric measures that accurately represent the target user population are essential to effective designing for human variability, yet the available anthropometric data are drawn from populations that are substantially dissimilar from the target populations for most products and environments. Many designs for civilians are assessed using military anthropometric data, for example, because the only available data with sufficient detail were gathered from soldiers. Recent studies have collected detailed information from civilians, but these populations are also not representative of typical user populations. This paper presents a new statistical method for applying the available anthropometric data to estimate distributions of anthropometric data for a target population. For many design problems, the target population can be characterized by overall demographic information, such as the gender ratio, age distribution, nationality, and ethnicity. Principal component analysis and linear regression are used to create statistical models that can draw on information from two or more databases to estimate values for a large range of anthropometric variables. The approach matches the race/ethnicity distribution of the target user population and incorporates a stochastic component so that all relevant variance is retained. Finally, it generates populations of virtual users rather than summary statistics for individual variables. Summary statistics from the virtual populations are shown to be reasonable approximations for the actual measures. This work has application to a wide range of design problems, including the design of workspaces, vehicles, and medical devices. Key words: anthropometry, principal components analysis, human variability, spatial analyses Relevance to industry Accurate estimates of target user anthropometry are essential to spatial analyses in artifact and task design. This paper presents a methodology for using available data to synthesize new populations that better represent target users. Application of these methods in the design and assessment of products and environments will produce more accommodating designs that promote better performance and customer acceptance. Introduction Effective design for human variability requires estimates of the distribution of user characteristics. For many design problems, body dimensions are critically important determinants of the safety and performance of the design. Consequently, anthropometric data are referenced in the design and evaluation of many products, and anthropometric analyses are central to ergonomic analyses of workstations. Corresponding author addresses: parkinson@psu.edu (Matthew B. Parkinson), mreed@umich.edu (Matthew P. Reed) The most common methods for applying anthropometric data to assessment of a product or workspace design have remained unchanged for decades (Roebuck, 1995). The ergonomist, designer, or engineer identifies human body dimensions that are related to the design problem. For example, seated hip breadth is related to the required width for a chair. The designer will then search for information on the selected anthropometric measures for the user population in a reference text or human factors handbook (Jung et al., 2009; Salvendy, 2006; HFES 300 Committee, 2004; Peacock and Karwowski, 1993). For the chair design problem, the designer might identify 95 th - percentile hip breadth for U.S. adult female office workers as the desired value and attempt to find that value in a handbook such as Humanscale 123 (Diffrient et al., 1981). This look-up table approach to applying anthropometric data has many limitations. The most critical issue is the assumption that the underlying database accurately represents the target user population. Unfortunately this is typically not the case. The sources of tabulated data are often not thoroughly specified, the sampled population is different from the target user population, or the Preprint submitted to International Journal of Industrial Ergonomics June 13, 2009

2 probability density function ANSUR CAESAR NHANES BMI Figure 1: The body mass index (BMI) distributions for women in two commonly used databases (ANSUR and CAESAR) and the general U.S. population (NHANES, ). data were gathered decades prior to the time of application. Anthropometric design guides are often based on military studies, which have historically been the most detailed anthropometric studies. For example, the Business and Institutional Furniture Manufacturers Association (BIFMA) anthropometric design guidelines for office chairs and workstations (BIFMA, 2002) makes extensive use of data from the 1988 U.S. Army anthropometric survey known as ANSUR (Gordon et al., 1989). The published ANSUR data, with over 100 anthropometric variables measured on over 4000 subjects, provide an excellent reference on body dimensions for the U.S. Army as of the late 1980s, but the ethnicity, age, and fitness of the ANSUR population is quite different from the general U.S. population or the likely user population for any civilian product or workspace. The published ANSUR data are even inappropriate for current or future military vehicles or equipment, because the U.S. military population has changed since 1988 (Lockett et al., 2005). Figure 1 illustrates the differences in distributions of body mass index (BMI) a measure of weight-for-stature calculated as body weight in kg divided by stature in meters square in women within two commonly used databases and the general U.S. population. Anthropometric surveys are expensive and timeconsuming, so it is rare for a designer to have access to anthropometric data drawn from the specific user population of interest. However, other reasonably accurate descriptions of the user population might be available. For example, ergonomists designing assembly workstations may have good distributional information on the gender, age, and race/ethnicity of the worker population. Similar demographic data for the target users of a consumer product might also be available from market research. The two standard approaches for modifying an existing dataset to be representative of a desired population are downsampling and weighting. The published ANSUR data are from a subset of 1774 men and 2208 women down- 2 sampled from the nearly 9000 men and women originally measured (Gordon et al., 1989). The subset was chosen such that the composition of the subset matched the characteristics of the Army on age and race/ethnicity. This is a powerful approach, provided that a large enough raw sample is available for each of the demographic subgroups. The second major approach assumes that each individual in the dataset is randomly selected from among those individuals in the target population who have the same demographic characteristics. A sampling weight is assigned to each individual, representing the fraction of the target population matching this individual s characteristics. Methodologies for weighting survey data are well developed and are applied in many domains, from crash statistics (National Center for Statistics and Analysis, 2007) to health surveys (U.S. Centers for Disease Control and Prevention, 2008). The best available statistics on the basic anthropometric characteristics of people in the U.S. are gathered as part of the National Health and Nutrition Examination Survey (NHANES), which uses U.S. Census data to compute sampling weights for each sampled person (U.S. Centers for Disease Control and Prevention, 2008). The NHANES data have been used to weight detailed anthropometric data from the Civilian American and European Anthropometric Resource (CAESAR), a survey incorporating traditional and body-scanning techniques, to be better representative of the U.S. population (Harrison and Robinette, 2002; Robinette et al., 2002). One version of the weighting procedure involves the creation of regression models relating descriptors such as stature to specific measures of interest such as buttock-popliteal length or sitting height. Secular and other changes in the population which are captured in the descriptors can then be used to extrapolate new values for the specific anthropometry (Drillis and Contini, 1966; Kinghorn and Bittner, 1995; Fromuth and Parkinson, 2008). More complex models such as neural networks have also been used (Dursun Kaya et al., 2003). A parametric approach to population weighting has been applied by Flannagan et al. (1998) in the development of functional human performance models. A linear regression predicting the desired dependent measure from an overall human dimension (usually stature) is convolved with a normal distribution representing the single-gender stature distribution of the target population. This approach is generalized from the univariate case to multivariate prediction as part of the current work. This paper presents a new methodology for using available demographic information to re-analyze existing anthropometric datasets to produce a new, synthesized dataset that better represents the target user population. A two-phase analysis is conducted that combines data from large-scale surveys with only a few variables (e.g., NHANES) with data from detailed studies (e.g., ANSUR or CAESAR). The method is fundamentally a weighting procedure, but it exploits the correlations among anthropometric measures to produce better estimates of the

3 distributions of variables than are obtained using typical weighting procedures. Methods Overview The methodology (Figure 2) obtains accurate estimates of the distributions of anthropometric variables for a target user population by synthesizing a virtual user population, each member of which is defined by a vector of attributes (gender, race/ethnicity, age, etc.) and the anthropometric variables of interest. An important consideration is that the subgroups of the user population will tend to have different relationships between individual body dimensions and overall dimensions. For example, the relationship between hip breadth and body weight is different for men and women. Ideally, a separate analysis would be conducted for each cell in the demographic matrix, but, in practice, sufficient data are available only for separate analyses by gender. Consequently, a method is needed to make the aggregate detailed database as representative as possible of the target user population with respect to demographic variables. First, distributional information is obtained for the target user population on as many variables as possible. Usually these data are categorical (gender, race/ethnicity, geographic region, occupational category, etc.) or continuous variables binned to be discrete (e.g., decile age bands). Ideally these data are crossed (the percentage of women separately for each age group, for example), but the method can proceed with only the marginal distributions. A large database is used to characterize basic anthropometric measures for each user category. For products and workspaces intended for users in the U.S., NHANES provides the best available data on stature (erect standing height), age, and body weight. Separately, the covariance structure of at least one detailed database such as ANSUR is analyzed using principal component analysis (PCA). PCA expresses the anthropometric measures Y in a new orthonormal basis (Jolliffe, 2004). Linear regression analysis is applied to predict the principal component scores (coordinates relative to the new basis) as a function of the overall body dimensions obtained from the large database. The regression models are used in a stochastic convolution procedure to convert estimates of the distributions of overall anthropometric measures for subgroups into distributions of principal component scores. The principal component transformation is then inverted to obtain a complete set of predicted anthropometric variables for an individual in the target population. The stochastic sampling procedure is repeated until the desired number of individuals in each subgroup has been generated to achieve the appropriate distribution on the original categorical variables. 3 Building a PCA Model Principal components analysis is used to establish the relationships between the selected anthropometric variables from the detailed database and overall body dimensions. The number of basis components is equivalent to the number of factors (e.g., anthropometric measures such as stature and arm length), but the method is most effective when most of the variability in the data is accounted for by the first few components. These characteristics make it a useful technique for modeling anthropometric data where the many measures are strongly correlated with a few predictors such as stature and body mass index. For a review of PCA see Jolliffe (2004) or see Lockett et al. (2005); Nicolay and Walker (2005) for anthropometric applications. The method begins with the analysis of a detailed anthropometric database U containing detailed measures for N U individuals. For target populations within the U.S., the database might be ANSUR or CAESAR, for example. The chosen database should be as close as possible to the target population with respect to distributions of age, ethnicity, and other variables, but in practice only a few detailed databases are available. The matrix U consists of X U and Y U, matrices corresponding to the predictors and measures of interest within the database. Consider the scenario in which stature and BMI are predictors: the N U stature and BMI pairs within U and a column of ones would comprise the N U j (where j = 3) predictor matrix X U. Similarly, Y U is the N U k matrix of detailed anthropometry for the k measures of each individual in the database. The detailed anthropometric data are centered by subtracting their mean Ŷ U = Y U Y U (1) The rows of Ŷ U are sorted into bins defined by the demographic characterization of the target user population (for example, gender age race). The data within each bin are then upsampled (a randomly selected row is duplicated) until the percentages across the bins are equivalent to those in the target population. This strategy is adopted rather than a downsampling procedure in order to make use of all individuals in the detailed dataset. This step is important because the correlation among anthropometric variables differs with age, race/etnicity, and other demographic variables. For each gender, k (e.g., 40) anthropometric measures of interest within the upsampled detailed data Ŷ U are analyzed using PCA. This determines P, a k k matrix of coefficients or loadings and Q, the N U k matrix of principal component scores. Each of the columns of Q corresponds to a principal component and each of the components is orthogonal to the others. These columns are sorted in descending order by the amount of variance within the data that they explain. A regression analysis is then conducted to identify the components that are significantly related (p < 0.05) to the overall body dimensions in X U. Typically, the first several components are associated with stature and/or BMI. These (z) significant components are selected for the creation of the predictive model. A reduced matrix of scores Qr is taken to be the first z columns of Q.

4 Target Population demographic data (distributions of gender, age, and ethnicity) synthesized body dimensions Representative Database (e.g., NHANES) - compute distributions of overall dimensions (stature and BMI) by demographic group Detailed Anthropometric Database (e.g., ANSUR) - sort by demographic data - upsample to match target demographics - PCA and regression Predictive Model detailed body dimensions as a function of overall dimensions (stature and BMI) Figure 2: A flowchart depicting the methodology used to obtain the synthesized anthropometry. A transformation matrix M, which relates the predictors to the reduced set of PCA loadings is then M = Q r X U. (2) Because each of the principal components is orthogonal, this is equivalent to performing a least-squares fit on each of the individual components of Qr, i.e., the standard linear regression fit. The residual variance s 2 r in the regression is calculated for each variable. Using M, the Nnew z scoring matrix Qnew, corresponding to arbitrary values of the predictors Xnew, can be calculated: where Qnew = XnewM + E. (3) E = [ɛ 1, ɛ 2,..., ɛ N new ] (4) The error terms ɛ i are normally distributed independent random variables with variance s 2 i. Because the k columns of Qnew are independent, this is equivalent to performing k simple linear regressions. Incorporating the Residual Variance Two people with the same stature and BMI will usually have considerable differences in their other body dimensions. The difference between the actual measures and the deterministic values predicted with the regression equations is called residual variance. The magnitude of the the residual variance is an indicator of the degree to which the synthesized anthropometry of two individuals with the same predictors (e.g., stature and BMI) might be expected to differ. A stochastic component is added to each of the predicted measures to account for the residual variance using a methodology similar to that in (Flannagan et al., 1998; Parkinson and Reed, 2006; Parkinson et al., 2007). For the first z components the stochastic element was incorporated back into the calculation of the component scores Qnew by randomly sampling from a normal distribution with mean = 0 and a standard deviation equal to the square root of the residual variance for the regression fit on that component. The remaining y z principal components are not significantly associated with the predictor variables, yet they 4 represent meaningful variance in the detailed anthropometric data. Rather than approximating these with a multivariate normal distribution, variance on these components is incorporated into the > z columns of Qnew by sampling randomly from the columns i of Q for i > z. Note that these scores are independent. This non-parametric approach reduces reliance on the assumption that the PC scores are normally distributed and helps to avoid unrealistic values when creating a large virtual population. The mapping from PCA-space back to the actual anthropometric measures is performed by multiplying the PCA coefficients by Qnew (the PCA scores for the new predictors) and shifting the results by the means observed in the data Results Ynew = Y U + QnewP. (5) The method is demonstrated by synthesizing the female ANSUR anthropometry from the CAESAR data set. The ANSUR data were collected in the 1980s from a military population that differs in important ways from the CAESAR civilian data collected nearly twenty years later (Figure 1 with BMI comparison). Comparing a synthesized population with known data from a substantially different database provides an opportunity for validating the methodology and examining strengths and weaknesses. The results were compared to the actual ANSUR sample on a range of variables and the effects of variable selection on the outcomes were evaluated. Building the Model To provide a direct comparison to the ANSUR dataset, anthropometry for 2208 women were synthesized the same number as in the true ANSUR dataset. Both the CAESAR and ANSUR populations are young relative to the US population as a whole, so the distribution of age was assumed to be roughly equivalent. However, the distribution of race within the target user population (ANSUR) is substantially different from that found in CAESAR. As proposed in this new methodology, rather than building separate models or using race as a predictor, the CAE- SAR data were sorted by race and upsampled so that the

5 relative distribution across bins matches that in the target population: 51.6% White, 41.8% Black, 2.6% Hispanic, resulting in a population size of 1872 individuals. Following the methodology described above, a statistical analysis of the women in the U.S. CAESAR database U was used to determine the relationships between overall body size descriptors (stature and body mass index) X U and the specific body dimensions of interest, Y U. Body mass index was used in place of body weight as a predictor of overall body size and shape because it is less correlated with stature. The y = 14 measures of interest for the target population are identified in Table 1 and were selected to represent four types of measurements: lengths, widths, circumferences, and functional measures. Additional selection criteria included presence in both data sets and the similarity of measurement methods. Where measures of both the left and right side of the participant were taken for the CAESAR data set, only the value from the right side was used. Two additional measures from CAESAR, stature and mass, were also included stature is a predictor and mass is necessary to calculate the second predictor, BMI. One potential criticism of the use of PCA methods for this type of application is that the columns of the scoring matrix Q depend on the variables selected for Y U. In other words, a model derived from sitting height, knee height, and biacromial breadth data would produce different estimates of sitting height than one derived from sitting height, knee height, and chest circumference. To examine this effect, each of the 14 anthropometric measures was simulated by randomly selecting 7 other measures from the set and constructing Y U for each of 10,000 runs. In each run, PCA was performed on Y U to yield P and Q. The scoring matrix was reduced to Qr based on the number of components that explain a statistically significant (p < 0.05) amount of variance in the data. Across the 140,000 simulations, the number of components required to achieve this level varied from two to five, with z = {2, 3, 4, 5} in {1%, 39%, 56%, 4%} of the simulations, respectively. The residual variance from the least-squares fit of predictors to the two components was retained and added back into predicted scores as outlined above. Synthesizing the Population For this problem two predictors Xnew were considered: stature and BMI. In a typical design problem one might use a different database as the source for the stature and BMI distributions, selected to match the target user population (for example, using NHANES to represent U.S. civilians). For this example the stature and BMI values of the ANSUR population were used directly to focus on the ability of the new methodology to synthesize the relevant anthropometry. Using Eq. 3, the new scoring matrix Qnew, which includes residual variance, was calculated. The synthesized anthropometry, Ynew was calculated using Eq. 5. The synthesized population had Nnew = members, the same number as in the actual ANSUR female population. Each of the 14 measures was simulated 10,000 times using randomly selected sets of 8 total dimensions, producing a distribution of results for each dimension. The 5 th, 50 th, and 95 th -percentile values of each of the y measures were calculated for each of the 10,000 runs and averaged. These values are summarized in Table 1. For comparison the equivalent data from the original ANSUR data are listed along with the differences at each percentile. Table 2 shows the 90% error intervals as both the percent and size (mm) at three commonly used reference locations (5 th, 50 th, and 95 th -percentile). Values greater than an arbitrarily selected criterion of 3% are shown in bold. Discussion and Conclusions The model was accurate in estimating measures of length, breadth, circumference, and even the functional measure, thumbtip reach. Sitting height, which is known to vary as a function of stature across ethnicities, was estimated well using this methodology despite the substantially different ethnicity distributions of the ANSUR and CAESAR populations. This can be attributed to the upsampling procedure in which the model database (CAESAR) was essentially reweighted to match that of the ANSUR data. The hip breadth measure estimates were as a whole slightly too large. Although the procedure was able to account for the differences in the BMI distributions in the two populations, the differences in fitness level or muscle tone may have influenced the results for this variable. Consistent with this hypothesis, the bideltoid breadth measure, which is related to upper-body muscle development, was consistently underapproximated. One final noteworthy difference is in the acromion-radiale length, a measure that should not be affected by muscle development and which is consistently mm too short in the synthesized population. This bias error could be the result of measurement differences in the two data collection procedures from which the data were collected. From the data users perspective, the new method offers several advantages over previous approaches. First, it is explicitly multivariate, capable of synthesizing anthropometry for many individuals within a large population at a time rather than relying on look-up tables or static constants that provide univariate anthropometric information. Second, variability across race/ethnicity groups is incorporated by weighting to match the prescribed distributions in the target population. If there are concerns that other demographics such as age might affect anthropometric ratios, these factors can readily be considered in the same manner. Third, a virtual population of users, each with a complete set of the desired anthropometry, is created. This allows designers the opportunity to compute any statistic of interest, rather than being limited to

6 those in published look-up tables. More importantly, designers can now explore the correlations among measures to determine, for example, how shoulder breadth changes with sitting height in the user population. This can assist in identifying to what degree individuals disaccommodated on one metric are likely to be disaccommodated on another. In situations where the raw synthesized anthropometry might be overwhelming or burdensome, the results can readily be reduced to the traditional look-up table format, tailored to the target user population. New types of analyses can be performed using the methodology. For example, sensitivity analyses can be conducted to determine the effects of ethnicity or age distributions on population anthropometry. One might use the approach to answer questions such as How important is it to know what percentage of automobile assembly workers are Hispanic within 5% if one is interested in knowing the 5 th -percentile thumbtip reach within 10 mm? While the example in the paper is for the U.S. population, any data (e.g., CAESAR s European component) can be used to build the detailed database. This enables other applications such as weighting for national populations (Mokdad and Al-Ansari, 2009; Klamklay et al., 2008) for global products. The current approach differs substantially from previous applications of PCA for anthropometric data analysis, which have typically focused on the identification of a small number of boundary individuals (e.g., Lockett et al., 2005). The method uses PCA to facilitate the reweighting via regression of the detailed data by rendering it in an orthonormal basis. The result is a new population that preserves the covariance structure of the original while being shifted and scaled to represent the target distributions of key variables (stature and BMI in the example). The performance of the method rests on several assumptions. First, the correlation among measures is assumed to be the same by subgroup for the target population, representative population from which the predictors for the synthesized anthropometry are obtained (e.g., NHANES), and detailed population used for PCA (e.g., ANSUR or CAESAR). Similarly, the available anthro measures (typically stature and BMI) from the representative data are assumed to be sufficient. The methodology readily incorporates any available measures, but availability of data is often restricted to stature and body weight (from which BMI can be calculated). Fortunately these two measures have been shown to account for a large fraction of the variance in a typical diverse population (Manary et al., 1999). The PCA results are affected by the variables that are included, but the simulations presented here demonstrate that good accuracy can be obtained if the selected measures are reasonably chosen. For example, adding a large number of circumference measures to an analysis primarily focused on length dimensions would be inappropriate. Although the results for this case study show that the method produces accurate results for a realistic test case, 6 the complexity of the method and the numerous subjective choices to be made preclude a general error analysis. The overall accuracy rests on the reliability of a number of assumptions, including that the user population characteristics are accurately known. The PCA and regression models assume linear relationships in the underlying data, which is generally supported by anthropometric data. For some analyses focusing on measures related to body weight, modeling the square root of circumferences, or using the log transform of BMI, may produce slightly better results. The convolution process for incorporating residual variance in the first few principal component estimates assumes normality in the residuals, which is well supported by the data. Additional research is needed to determine when separate analyses should be conducted for subgroups that may differ in the relationships among anthropometric measures. The limiting factor is the number of representatives of the subgroup in the detailed database. Using ANSUR, a separate analysis for African-American women might be feasible (approximately 500 individuals), but insufficient data are available for Hispanic women. The overall importance of subgroup models is influenced by the representation of the subgroup in the target population. A more-detailed modeling of a subgroup that is only a small fraction of the target population will have a minimal effect on the resulting design. Acknowledgements The authors acknowledge Dr. Carol Flannagan of UMTRI for her insights into anthropometric analysis methods. This work was supported by the National Science Foundation under grant #

7 References BIFMA, Apr Ergonomics guideline for vdt (visual display terminal) furniture used in office work spaces. Clauser, C., McConville, J., Young, J., Weight, volume and center of mass of segments of the human body. AMRL-TR-69-70, Wright Patterson Air Force Base. Diffrient, N., Tilley, A., Bardagjy, J., Humanscale. The MIT Press. Drillis, R., Contini, R., Body segment parameters. Office of Vocational Rehabilitation Engineering & Science, New York, cited in Clauser et al. (1969). Dursun Kaya, M., Samet Hasiloglu, A., Bayramoglu, M., Yesilyurt, H., Fahri Ozok, A., A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system;. International Journal of Industrial Ergonomics 32 (2), Flannagan, C. A. C., Manary, M. A., Schneider, L. W., Reed, M. P., Improved seating accommodation model with application to different user populations. In: Proc. SAE International Congress & Exposition. Vol SAE, Warrendale, PA, USA, pp Fromuth, R., Parkinson, M., Predicting the 5th and 95th percentile anthropometric segment lengths from population stature. In: Proc. ASME International Design Engineering Technical Conferences. New York, NY. Gordon, C. C., Churchill, T., Clauser, C. E., Bradtmiller, B., Mc- Conville, J. T., Tebbetts, I., Walker, R. A., anthropometric survey of U.S. Army personnel: Methods and summary statistics. final report. (NATICK/TR-89/027). Harrison, C., Robinette, K., Jan Caesar: Summary statistics for the adult population (ages 18-65) of the united states of america. stinet.dtic.mil. HFES 300 Committee, Guidelines for Using Anthropometric Data in Product Design. Human Factors and Ergonomics Society, Santa Monica, CA. Jolliffe, I., Principal Component Analysis, 2nd Edition. Springer. Jung, K., Kwon, O., You, H., Development of a digital human model generation method for ergonomic design in virtual environment. International Journal of Industrial Ergonomics In Press, Corrected Proof,. Kinghorn, R. A., Bittner, A. C., Truck driver anthropometric data: Estimation of the current population;. International Journal of Industrial Ergonomics 15 (3), Klamklay, J., Sungkhapong, A., Yodpijit, N., E. Patterson, P., Anthropometry of the southern Thai population. International Journal of Industrial Ergonomics 38 (1), Lockett, J., Kozycki, R., Gordon, C., Bellandi, E., Proposed integrated human figure modeling analysis approach for the Army s future combat systems. In: Military Vehicle Technology (SP- 1962). SAE International, Warrendale, PA. Manary, M., Flannagan, C., Reed, M., Schneider, L., Human subject testing in support of ASPECT. Technical Paper SAE Transactions: Journal of Passenger Cars 108. Mokdad, M., Al-Ansari, M., Anthropometrics for the design of Bahraini school furniture. International Journal of Industrial Ergonomics In Press, Corrected Proof,. National Center for Statistics and Analysis, National Automotive Sampling System Crashworthiness Data System. Analytical User s Manual. U.S. Department of Transportation, Washington, DC. Nicolay, C. W., Walker, A. L., Grip strength and endurance: Influences of anthropometric variation, hand dominance, and gender;. International Journal of Industrial Ergonomics 35 (7), Parkinson, M., Reed, M., Kokkolaras, M., Papalambros, P., Optimizing truck cab layout for driver accommodation. ASME Journal of Mechanical Design 129 (11), Parkinson, M. B., Reed, M. P., Optimizing vehicle occupant packaging. SAE Technical Paper Peacock, B., Karwowski, W. (Eds.), Automotive Ergonomics. Taylor & Francis, London. 7 Robinette, K., Blackwell, S., Daanen, H., Boehmer, M., n Civilian american and european surface anthropometry resource (caesar), final report. volume 1. Tech. rep., United States Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio, U.S. Roebuck, J. A., Anthropometric methods: designing to fit the human body. HFES. Santa Monica, CA. Salvendy, G. (Ed.), Handbook of human factors and ergonomics, 3rd Edition. John Wiley & Sons. U.S. Centers for Disease Control and Prevention, National Health and Nutrition Examiniation Survey (NHANES). National Center for Health Statistics. URL List of Figures 1 The body mass index (BMI) distributions for women in two commonly used databases (ANSUR and CAESAR) and the general U.S. population (NHANES, ) A flowchart depicting the methodology used to obtain the synthesized anthropometry.. 4

8 Table 1: Comparison of the summary statistics for the true and synthesized populations of ANSUR women. ANSUR women synthesized 5 th 50 th 95 th 5 th 50 th 95 th stature BMI acromion height, seated acromion height, standing acromion - radiale length biacromial breadth bideltoid breadth buttock - knee length, seated chest circ., under bust head breadth head length hip breadth, seated knee height, seated sitting height thigh circumference thumbtip reach Table 2: The 90% error intervals for the distributions of each measure. Error percentages greater than an arbitrarily selected value of 3% are shown in bold. 90% error interval (%) 90% error interval (mm) 5 th 50 th 95 th 5 th 50 th 95 th acromion height, seated acromion height, standing acromion - radiale length biacromial breadth bideltoid breadth buttock - knee length, seated chest circ., under bust head breadth head length hip breadth, seated knee height, seated sitting height thigh circumference thumbtip reach