Posture and anthropometry

Chapter 25

Posture and anthropometry Russell Marshall and Steve Summerskill Design Ergonomics Group, Loughborough Design School, Loughborough University, Loughborough, United Kingdom

1. Introduction Anthropometric data, essentially data on human body size, are the basis upon which all digital human models are constructed. They also serve as the key variable in human product/workstation evaluations and design. The size of a single person will inherently define the way in which they will be able to interact with their environment or, conversely, the requirements of the environment to accommodate them. Thus, in a simple example of a doorway, there are essentially two critical dimensions: height and width. If the doorway is lower than the height of a person, that person will need to duck to pass through, and if the doorway is narrower than the width of a person, they may have to turn sideways or may have to squeeze through. This is easily managed for a single person; the dimensions of the doorway can be specified to the relevant body dimensions to ensure accommodation. However, products, workstations, and environments are rarely bespoke designs tailored to the needs of any one individual; they are intended to accommodate whole populations irrespective of their size and shape. In the example of the doorway, the height and width should therefore be specified to accommodate the upper design limits to ensure all are accommodated. Digital human models are typically used in two main ways: in a proactive manner or in a reactive manner. This essentially maps onto a typical design process: early on, when requirements are being managed and specifications determined in a proactive mode; or much later, when the design is fixed, or even implemented and in-place in a reactive mode. In the reactive mode, an all too common occurrence for ergonomics interventions, an evaluation has to be made of an existing design. This situation is normally triggered by a problem being revealed with a product or workplace already in service, and therefore, there are limited opportunities to influence the situation and to ensure appropriate accommodation of all “users.” Here, the situation is one of reporting the issues and hopefully feeding in to future improvements or redesigns. In the proactive mode, evaluations can help set the specifications of the design and ensure the desired range of users are accommodated from the outset. This latter mode is clearly advantageous and can lead to well-considered designs that have significant advantages for the people that interact with them, both in terms of desirable characteristics such as ease of use and comfort and also more essential characteristics such as safety and healthy working postures. It is therefore essential that in a design process in which digital human modeling (DHM) is contributing specifications to accommodate a given population, that particular care is given to the characteristics of that population. This chapter considers some of the issues associated with anthropometry in that process.

2. Understanding and working with human body size and shape data 2.1 Anthropometric variability The size and shape of human beings are infinitely variable, and methods have been developed to categorize, classify, and help manage this variability for various applications. A number of authors over time have highlighted the challenges faced in attempting to understand and manage anthropometric variability, either in the design process or in the evaluation of existing products or workstations. Owing to the inherent complexity of this field, a typical approach is one of reductionism, simplifying the all too overwhelming diversity of the human population into conceptually more manageable models. Unfortunately, these simplifications often lead to misconceptions. One of the earliest studies by Daniels (1952) highlighted “The tendency to think in terms of the ‘average man’ is a pitfall into which many persons blunder when attempting to apply

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human body size data to design problems.” Later, Pheasant (Pheasant & Haslegrave, 2006) published his five fundamental fallacies in design, the second of which reads “This design is satisfactory for the average person; it will therefore be satisfactory for everyone else.” The foundation of this fallacy is the common interpretation of statistical data and the misunderstanding that “average” means “most.”

2.2 Issues to consider when working with anthropometric data 2.2.1 Percentiles Anthropometric data are typically measured as linear distances (for example, sitting height, arm length, hip breadth, etc.) or girths (for example, hip circumference, thigh circumference, etc.) in standard units such as millimeters. However, when presenting data from a large data set, the data are primarily presented using percentiles. Anthropometric data often form a Gaussian (normal) distribution when considered in a sufficient sample size. Fig. 25.1 shows the distribution of anthropometric data for one of the most common anthropometric measurements, stature, in this particular case UK male adult stature (Peebles & Norris, 1998). The data are distributed evenly about the mean and range from <first percentile to >99th percentile. Percentiles can be interpreted such that for any given percentile p, p% of the population will have a measurement less than p, and 100-p will have a measurement greater than p. Thus, the average or mean stature merely identifies that 50% of the population will be shorter than the mean, and 50% will be taller than the mean. Percentiles are conceptually easy metrics to use when considering single measurements or “univariate” problems. However, when more than one measurement is concerned in so-called “multivariate” problems, then percentiles can become problematic. Percentiles are used to help manage the variability of a given dimension. They do not replace the measurements described earlier but rather provide a means of understanding where within the range, a given value sits or conversely provides a value for a position within the range. For example, first percentile UK male adult stature still equates to a linear value of 1592 mm (Peebles & Norris, 1998). In design terms, percentiles provide a means to establish specifications with the understanding of where a design requirement lies within the target population data. If this is applied to our earlier doorway example, if the requirement was to accommodate 99% of the population for doorway height, the 99th percentile can be used to identify the specification for the height in mm, knowing that 99% of the population will be shorter than that value and thus able to pass through the door unhindered.

2.2.2 Correlation As already suggested, anthropometric data are very straightforward when dealing with a single measure at a time, or univariate problems. However, most design problems are multivariate; they have many dimensions that need to be considered. A common approach is to determine a limit of accommodation for one dimension and then consistently apply that to all dimensions relevant to the given problem. Thus, if 99th percentile is applied to door height, it would also be

FIGURE 25.1 Normal distribution of stature in UK males. Data from Adultdata Peebles, L., & Norris, B. (Eds.). (1998). Adultdata. The handbook of adult anthropometry and strength measurements e data for design safety. Department of Trade and Industry.

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TABLE 25.1 The Fallacy of the ‘Average Man’. Cumulative number of people from a total sample of 4063 men who exhibit average characteristics across 10 clothing dimensions (Daniels, 1952). Dimension

Range, defining average (cm)

No. included

Percentage of sample

Stature

173.95e177.95

1055

25.97

Chest circumference

96.95e100.95

302

7.43

Sleeve length

83.95e86.95

143

3.52

Crotch height

81.95e84.95

73

1.8

Vertical torso circumference

162.95e166.95

28

0.69

Hip circumference (sitting)

103.95e108.95

12

0.3

Neck circumference

36.95e38.95

6

0.15

Waist circumference

78.95e83.95

3

0.07

Thigh circumference

54.95e57.95

2

0.05

Crotch length

69.95e72.95

0

applied to door width and so on. However, the temptation is to assimilate these multiple dimensions into human models. Resources such as Dreyfuss’ “Measure of Man” (1967) provide access to anthropometric data for design purposes. In the Measure of Man (in later editions the Measure of Man and Woman), univariate humans are presented, including “Jack” and “Jill,” two full-sized dimensioned drawings of a 50th percentile adult US male and female (Porter, Case, Marshall, Gyi, & Sims, 2004). Unfortunately, these are again a simplification and can lead to the misinterpretation that people come in standard sizes. The underlying problem ultimately revolves around the correlation between anthropometric measures. It may be considered reasonable to expect that when dealing with a given percentile for a single dimension such as 50th percentile stature and that this would correlate with 50th percentile arm length, 50th percentile hip breadth, and so on. However, the reality is that the variability between the percentiles associated with each body dimension will vary significantly for any one individual and that correlation between the dimensions is often poor. Correlation is typically presented as a value between 0 and 1, with 0 having no correlation and one being perfectly correlated. Perfect correlation is a direct linear relationship such that it would be possible to accurately predict the size of one anthropometric variable from another. Unfortunately, when considering whole populations, correlations are generally low. For example, in a study of 2000 UK car drivers (Haslegrave, 1980), no correlations were found greater than 0.82 and most were less than 0.5. This characteristic of anthropometric data for populations presents significant challenges when trying to use the data for design and evaluation purposes. As already discussed, Daniels highlighted the fallacy of the average man, what this means is that while it is possible to find a person with a 50th percentile stature, for example, the probability of that person being 50th percentile for other body dimensions decreases rapidly the more dimensions that are considered. Table 25.1 shows the results of a study conducted by Daniels (1952) of 4063 Air Force personnel that highlights this fact. The data show that across 10 clothing related measurements, 1055 Air Force personnel are of average stature, 302 of the 1055 are also average for chest circumference, 143 of the 302 are also average for sleeve length, and so on. By the time 10 measurements are considered, none are average for all measures. The reality of anthropometric diversity is also shown in Fig. 25.2 where across 12 anthropometric dimensions, the percentile for any one individual can vary greatly (Porter & Porter, 2001). The implication for this variability is that not only average (50th percentile) people but also 5th, 95th, or any other singular percentile people do not exist. This is particularly problematic when the literature, including anthropometric databases, standards, and other sources, can be interpreted in a way that suggests that they do.

2.2.3 Standardized measurements One of the important assumptions when interpreting and applying anthropometric data is that data from different sources are comparable. It would be reasonable to expect that sitting height in one database provides data on the same measurement as sitting height in another. To achieve this, anthropometric data are highly standardized measures, refined over time to aid accuracy and repeatability of data collection. These are what are known as static anthropometric data. Standards such as ISO 7250e1:2017 (British Standards, 2017) govern the definition of anthropometric measurements including the

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FIGURE 25.2 Percentile values of nine male subjects for a variety of body dimensions (Porter & Porter, 2001).

identification of key landmarks on the human body and the methods by which the measurement is captured. ISO 15,535:2012 (British Standards, 2012) then details the further requirements for establishing an anthropometric database including quality control and statistical processing. The result of this standardization is that data can be obtained from a variety of sources or even collected specifically for a given application with the confidence that the data are unambiguous. This standardization is however not without its shortcomings for the practitioner. The standardization of anthropometric measurements is entirely performed to support the process of data collection. Traditionally, this would mean the use of a tape measure or dedicated piece of anthropometric measuring equipment such as an anthropometer or stadiometer to gather data. More recently, these data would be extracted from a three-dimensional (3D) body scanning system such as those offered by Size Stream (2018), [TC]2 (2018), or VITRONIC (2018). To aid in repeatability, measures are typically collected using human landmarks. For example, stature is defined as the “vertical distance from the floor to the highest point of the head (vertex)” (British Standards, 2017). This simple measurement is relatively straightforward to capture as the floor and the top of the head are easily identifiable. The only potential source of error then comes from the posture of the person being measured and thus the method requires that the subject is fully erect, the feet are together, and the person is looking straightforward. However, many anthropometric measures are much less straightforward. For example, arm length is defined as the measurement “from the bony tip of the shoulder (acromion) to the tip of the outstretched middle finger” (Peebles & Norris, 1998). In this instance, the most lateral edge of the scapula otherwise known as the acromial process is used as a landmark to approximate the location of the upper arm joint. The acromion to fingertip is therefore an approximation of the length of the arm. As the acromion is a generally clearly identifiable landmark, arm length is generally a robust anthropometric measurement with little ambiguity. Other measurements do not have such clear landmarks and thus can provide challenges for the practitioner. One example of this is waist circumference. ISO 7250e1:2017 defines this as the “horizontal circumference of the trunk at a level midway between the lowest ribs and upper iliac crest” (British Standards, 2017). Adultdata suggests this is “measured horizontally at the level of the waist (where the smallest abdominal circumference occurs)” (Peebles & Norris, 1998). The National Health and Nutrition Examination Survey (NHANES) in the US defines this at the level of the iliac crest (CDC, 2017). In this instance, the complexity is one of definition, and so anthropometric measures should always be checked for compatibility if more than one source is being used, and the definition of a specific measurement should never be taken for granted. In some cases, the complexity of defining a given measure has been noted and thus more comprehensive anthropometric databases may provide multiple versions, using different definitions. However, the key challenge is not the definition of the measurement and thus the data collected; it is how applicable these data are. As discussed, anthropometric data are collected to aid data collection, not to aid application for designers, ergonomists, and other practitioners. Thus, in the case of arm length, how useful is the value

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FIGURE 25.3 Functional and dynamic anthropometry. Left, forward pinch grip reach. Right, reach envelopes at a specific worktop height in the SAMMIE DHM system (Case, Marshall, & Summerskill, 2016).

that provides the maximal length of a human arm fully outstretched? If a designer wishes to know where to place a control such that is can be reached, does the length of the arm actually help? How many controls are activated with a fully outstretched arm with the fingertip? What if the control requires a palm grip, such as a steering wheel? It is at this point that the application of anthropometric data becomes significantly more complex. Not only do the issues of the applicability of the anthropometric data pose challenges but in the case of the arm length and reach, the location of the shoulder would also need to be known; this would be governed by other anthropometric measures, and posture, which would in turn be governed by the task being performed. In certain cases, some of the common issues have been addressed. Dynamic anthropometry is concerned with the measurement of humans performing tasks; thus, if the application requires a grip reach, then there are anthropometric measures such as forward pinch-grip reach or more usefully, so-called reach, or working envelopes (Kroemer & Grandjean, 1997), as shown in Fig. 25.3. However, there are other issues for which the solutions are less well understood and data are limited. One of the most overt issues is that nearly all anthropometric data are collected seminude; thus, the subjects are only wearing underwear for the majority of data collection. From an applicability perspective, this clearly has significant limitations. For most applications, some understanding of basic clothing would be a requirement. This can become even more critical in situations where nonstandard clothing and equipment are common, workers or emergency service personnel using personal protective equipment (PPE) for example, or military personnel wearing body armor and equipment. In these instances, traditional anthropometry is of limited value.

2.2.4 Database characteristics There are a number of other issues that users of anthropometric data should consider. One common concern is the age of the data available. Anthropometric data collection is an inherently expensive process both in terms of time and money. As such, large-scale data collection is not a common occurrence and so many of the data sources that are typically available are often quite old. For example, one of the more common anthropometric databases in the UK is Adultdata, published by the Department of Trade and Industry (Peebles & Norris, 1998). Adultdata is a compendium of data from other sources drawn together in one volume (including Bodyspace (Pheasant & Haslegrave, 2006) and Peoplesize (Open Ergonomics, 2008) among others). Data are available for 266 physical body measurements for multiple nationalities. Adultdata was published in 1998, but the sources of data within it range from 1969 to 1998. The data have been statistically treated through ratio scaling to factor in increases in stature and weight in many of the world’s populations over this time period; however much of the data are, at best, 20 years old. Another highly cited source is the Army Anthropometry Survey (ANSUR) that provides data collected between 2010 and 2012 in its most current form (Gordon et al., 2014); the previous version was from 1988 (Gordon et al., 1989). The age of data is therefore a potential limitation in it applicability to current evaluations. In some cases, the change over time may be significant, for example, secular growth of certain populations and

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in particular the increase of body mass index (BMI) and the corresponding change in body shape (NCD Risk Factor Collaboration (NCD-RisC)*, 2016). Naturally, many of the sources are also considered to be commercially valuable and so can only be accessed for a fee, and in some cases not at all. Surveys such as Size UK (Bougourd & Treleaven, 2010) and the Civilian American and European Surface Anthropometry Resource (CAESAR) (Robinette, Daanen, & Paquet, 1999) are examples of comprehensive anthropometric databases that can cost thousands of dollars to access. This may place rich sources of data out of the reach of some practitioners. For some of the available data sources, there will be limitations in the dimensions present. Many of the more regular surveys, such as NHANES in the US (CDC, 2017) and the Health survey for England (NatCen Social Research & University College London. Department of Epidemiology and Public Health, 2017), only contain basic measures such as stature. Thus, although they are up to date being collected on an annual basis, they have limited application. Others may be much more comprehensive but may be missing vital measures for a given application. It is possible to synthesize missing data from other measures, and the success of this will depend on the correlation; the stronger the correlation, the more robust one measure is a predictor of another. However, as has already been noted, correlations are generally quite poor. All data sources should be evaluated for sample size. Because the data in question are likely to be used to evaluate the accommodation of a population, a robust sample size is important. There are again very few simple answers regarding how many are sufficient, but the smaller the sample is, the less likely it is to truly reflect the full anthropometric diversity of the given population. For specific applications where a smaller data set is to be used; for example, where the practitioners may collect their own data, a smaller sample can be appropriate, especially if this can then be placed into the context of national data. In these cases, participants can be identified as having particular percentiles and so the data can be collected to ensure there are representative data within key areas, e.g., >90th and <5th percentiles, to ensure the extremes as a minimum are being evaluated. For example, in a study by the UK Rail and Safety Standards Board, anthropometric data were collected from 109 train drivers to understand the relationships between this specific population and national data and to explore the real-world body proportionality of train drivers within the DHM evaluations (RSSB, 2007) (Summerskill, Bird, Case, Porter, & Marshall, 2008). A further consideration is the population the data were collected from. The population’s characteristics are all potentially relevant depending on the application: gender, nationality, occupation, and age. In some cases, anthropometric data may only be available from a narrowly defined population and this must be taken into account when applying the data. There are no hard and fast rules; the scarcity of data makes the application of data from another population a common occurrence. However, it is important that the data being used are compared to the best understanding of the target population. Thus, if for example, ANSUR (military) data were to be used for a civilian application, it would be important to recognize that the military population is likely to be skewed toward leaner, more muscular physiques with the accompanying anthropometric measures (Marras & Kim, 1993). Many databases do not capture data from older adults and thus do not factor in changes to body size and shape as people age. Adultdata and CAESAR do not have data on people who are older than 65 years. SizeUK does have data of people up to the age of 91 years and older and Adultdata (one of the Adultdata series) (Smith, Norris, & Peebles, 2000) has data of people older than 90 years for some nationalities. The final consideration in this issue concerns the ability of the population. Most anthropometric databases are collected from “healthy” able-bodied adults. As such they contain little or no information on people with disabilities. For most applications aimed at a general population, this is a significant omission and presents a considerable challenge for practitioners. Some databases do exist containing anthropometry from disabled populations; however, they tend to be from samples of limited size or with other limiting factors (Das & Kozey, 1999; Goswami, Ganguli, & Chatterjee, 1987; Hobson & Molenbroek, 1990; Paquet & Feathers, 2004; Steenbekkers & Van Beijsterveldt, 1998). Whilst all of the aforementioned limitations are often explicitly stated in databases, the challenge lies in quantifying the impact these limitations have if the data are subsequently used in evaluations or to inform design. Equally some of these issues can be addressed through the application of these data using DHM.

2.3 The use of anthropometric data for digital human modeling Digital human models rely on anthropometric data to size the human appropriately. Regardless of the form complexity of the human model in question, anthropometric data are used to determine the appropriate body dimensions to represent people within a given population. Typically, human modeling systems have been developed recognizing some of the challenges discussed previously. They may therefore come with their own anthropometric data sources, often based on the existing major databases such as ANSUR and Adultdata. In addition, they provide the means to specify human size in a simplified manner. For example, some systems allow univariate human models to be specified merely by specifying a

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percentile for a given nationality and gender. For example, the SAMMIE DHM system (Case et al., 2016) allows univariate human models to be created based on a stature percentile or a stature measurement that is then converted into a percentile. This generates a human with the specified percentile for all body dimensions. In addition, DHM systems also typically allow individual body dimensions to be varied to some degree and some provide complete freedom such that customized humans can be created. This may be enabled through the specification of a standard anthropometric measurement such as buttockeknee length or through the provision of a complete human form from a 3D body scanning system. Finally, some systems offer a hybrid approach where a limited set of characteristics can be specified and other measures are derived from these top-level metrics. For example, the Rechnergestütztes Anthropometrisch-Mathematisches System zur Insassen Simulation (RAMSIS) typology approach (Bubb et al., 2006; Wirsching & Premkumar, 2007) allows length, proportion, and corpulence to be defined by the practitioner from a simplified list. This results in a human model with statistically likely body dimensions based on the three specified characteristics. The specification of human models using these different approaches will depend on what the practitioner aims to do. Basing human models or specific measures on percentile data provide an understanding of the interaction of a given percentage of the population with the product or workplace being investigated. Basing human models on data captured from real people provides an understanding of the interaction of specific people with a product or workplace. Using hybrid approaches such as the RAMSIS typology gives an understanding of the interaction of broad categories of people, e.g., tall, long torso, and slim waist with a product or workplace.

2.3.1 Percentile accommodation evaluation approaches One of the main limitations of DHM systems is that they are not able to guide the practitioner in how to manage the complexities associated with anthropometric variability. This has to come from the expertise and experience of the user of these tools. It is therefore critical that these tools are used in full understanding of the caveats of working with human models if valid findings are to be produced. DHM systems are extremely powerful and convincing visualization tools, and it is as easy to create a convincing but fundamentally flawed evaluation as it is an evidence-based and statistically robust evaluation. One of the most common evaluation approaches used for more than 50 years in the field of engineering and design is the use of design limits based around 5th and 95th percentile human models, sometimes with an additional 50th percentile human. These limits are still supported as an approach even today by inclusion in a range of standards such as BS EN ISO 14,738 (British Standards, 2008), PD CEN/TR 16,823 (British Standards, 2015), and BS EN ISO 15,537 (British Standards, 2004). The basic understanding is that if an evaluation is performed representing a small person (5th percentile) and a large person (95th percentile), 90% of the population will be accommodated if the population is for a single gender or 95% of the population if the population contains males and females. Where in the latter case, the 5th percentile limit is typically female and so with a 50:50 male female split, 2.5 of the total population is excluded by accommodating a 5th percentile female and above, and the 95th percentile limit is typically male and so 2.5% of the population is excluded by accommodating 95th percentile male and below. As discussed earlier, this can be true if considering a single dimension or univariate problem. For multivariate problems, it is far from true (Porter & Porter, 2001). As such, this approach has significant limitations and should only be used with care. One of the first concerns is the fact that univariate human models as previously highlighted are statistically impossible, despite the fact that they can easily be created in DHM systems. In a study by McConville and Churchill (1976), it was shown that if 95th percentile vertical measurements are added together for 14 measurements that span from floor to the top of the head, the total was approximately 300 mm greater than the stature for a 95th percentile person. The left human model in Fig. 25.4 shows the 14 measures A to M, and Table 25.2 the corresponding data for female Air Force personnel. The same phenomenon occurs when a seated human model is specified using univariate percentile data, for example, sitting height, buttockeknee length, and knee height. When the human model is then placed in a standing posture, its stature will not be 95th percentile. The right human model in Fig. 25.4 shows the three seated measures, one to three, and the stature measure 4 in Table 25.2 shows the corresponding data. Thus, some of the measurements in the human model have to be modified by the system to ensure the model can be internally coherent. Some of these issues are associated with the construction of humans and human models and incompatibilities between standard anthropometric measures and their application in the construction of human models. Humans and human models consist of a skeleton, upon which flesh is supported. As discussed earlier, many anthropometric measures rely on bony landmarks, and in many instances, these form a good approximation of the bone length within the external measurement. However, many anthropometric measurements also include a significant flesh component. For example, buttockeknee length is a very useful anthropometric measurement in human modeling as it can be used to define the length of the upper leg.

FIGURE 25.4 Left: 95th percentile female human model, stature is not the sum of individual vertical measures. Right: 95th percentile male human model, standing anthropometry not compatible with seated anthropometry.

TABLE 25.2 Data highlighting how percentile measures for individual body segments are not additive and are not compatible with overall measures such as stature for the same percentile (McConville & Churchill, 1976; Peebles & Norris, 1998). Var

Measurement

95th percentile (mm)

Var

Measurement

95th percentile (mm)

A

Floor to lateral malleolus level

78

1

Sitting height

980.0

B

Lateral malleolus to ankle level

68

2

Buttock-knee length

673.0

C

Ankle to tibiale level

344

3

Knee height

591.1

D

Tibiale to gulteal furrow level

348

Resultant stature*

1892.0

E

Gluteal furrow to crotch level

51

F

Crotch to buttock level

105

Stature

1869.2

G

Buttock to trochanteric level

39

H

Trochanteric to abdominal extension level

136

I

Abdominal extension to waist level

97

J

Waist to bust point level

219

K

Bust point to acromial level

168

L

Acromial to suprasternale level

24

M

Suprasternale level to cervicale level

94

N

Cervicale level to vertex

251

TOTAL (resultant stature)

2022

Stature

1721.5

O a

4

Resultant stature of 95th percentile seated human taken from SAMMIE (Case et al., 2016).

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However, it is not a direct measurement of the femur; it also includes a considerable portion of the buttocks. This can lead to some of the inconsistencies already highlighted between standing and seated human models and their anthropometry. For a given buttockeknee percentile, how much of that measurement is bone length and how much is muscle and fatty tissue in the buttocks? Thus, a 95th percentile buttockeknee length could be achieved by two very differently composed human models. Ultimately, percentiles do not represent individuals; they represent a probability distribution for certain body dimensions within certain populations (Högberg, 2005). Yet they are often used to generate individual human models. When used in the context of the 5th to 95th percentile approach, it is advised that multivariate problems are not explored solely by this technique. A number of authors have highlighted the limitations of this approach; Daniels work as shown in Table 25.1 highlights that for a 50th percentile approach, those excluded for one variable within a multivariate problem are not the same as those excluded for another, the exclusion is in fact cumulative. This is also true for the 5th to 95th approach. Roebuck et al. showed in 1975 that when designing from 5th to 95th percentiles, by the time 13 measures had been taken into account, the accommodation was down to nearly 50% (Roebuck, Karl, & Thomson, 1975). More recently, Herman Miller, the furniture company, highlighted that if they followed the 5th to 95th process in the design of their office chairs, rather than accommodating the desired 95% of the population after just four critical dimensions of the chair had been considered, accommodation would be down to 68% (Stumpf, Chadwick, & Dowell, 2007). Thus, great care must be taken when univariate human models are used in evaluations or for design purposes. They must only ever be applied with a full understanding of their limitations, and it is advocated that rather than 5th to 95th limits, the starting point should always be first to 99th.

2.3.2 Alternative accommodation approaches To improve the situation within DHM and its application by practitioners in performing evaluations, a number of approaches have been developed to manage the complexity of human diversity. Two of the most common take either a boundary case approach or a distributed case approach (Vinué, 2017). Fig. 25.5 shows a bivariate normal distribution of US Male stature and weight (Gordon et al., 1989) over which the 90th, 95th, and 99th percent confidence intervals (ellipses) have been plotted. These intervals represent the percentage accommodation aimed for. Boundary cases, in the form of socalled “boundary manikins” would be points toward the edges of the desired interval. Distributed cases would be spread,

FIGURE 25.5 Bivariate normal distribution of US male stature and weight (Gordon et al., 1989) with 90, 95, and 99th percentile confidence ellipses.

342 PART | V Postural interactions

FIGURE 25.6 Accommodation levels achieved across a range of measures comparing a 90% confidence hyperellipsoid with 5th to 95th percentile approaches (Brolin, 2016).

randomly, or though some strategic sampling throughout the interval. More than two dimensions can be considered using this approach, a third dimension creates a confidence ellipsoid, and further added dimensions form a multidimensional hyperellipsoid. The boundary manikin approach has significant advantages over more traditional percentile-based manikins. Brolin (Brolin, Högberg, & Hanson, 2012) compared both approaches in the design of a seated workplace (Fig. 25.6). The findings showed that as more dimensions are added and key anthropometric variables considered, the results for a percentile-based univariate manikin approach demonstrated identical behavior to that found by Roebuck et al. and Herman Miller discussed earlier. The percentage accommodated rapidly decreases the more dimensions that are considered. However, the results for boundary manikins generated from a multidimensional hyperellipsoid covering a 5th to 95th percentile range stayed relatively consistently around the 90% accommodation level. Unfortunately, the calculations become more complex and the number of boundary cases required to cover the confidence region can become overwhelming when many dimensions wish to be explored. This can be exacerbated further if distributed cases are also used to cover some of the region missed by a purely boundary approach. Therefore, methods such as principle component analysis (PCA) can be used to reduce the number of dimensions without losing the important variance of the data (Jolliffe, 2002; Meindl, Hudson, & Zehner, 1993; Reed & Park, 2017; Young et al., 2008). Yet, even with sophisticated reduction techniques such as PCA, there can be challenges in knowing which anthropometric variables are critical and which boundary or distributed points to use in evaluations. It is not unusual for PCA to still result in large numbers of human models to use in an evaluation. In the development of a spacesuit with an accommodation range of first percentile US female to 99th percentile US male, Young et al. (2008) identified 25 critical dimensions. This resulted in 26 human models for each gender which is a considerable workload for practitioners to manage when performing an evaluation. To aid in the process of managing the number of potential manikins generated from the application of PCA in a boundary or distributed approach to more complex multivariate problems, there have been a number of developments. As previously introduced, the built-in anthropometric specification process in RAMSIS revolves around three anthropometric characteristics of length, proportion, and corpulence. This is essentially a variant of a boundary/distributed manikin approach and has the benefit of reducing the variables to a manageable number and being inherently built into the DHM system, avoiding issues with practitioners having to perform the relevant calculations themselves. Another approach is that of Bittner (2000). This approach involved the development of a family of manikins known as the advanced cadre or A-CADRE, which were designed to support workstation design. The development process involved four key stages of processing anthropometric data, including identification of variables, development of intercorrelations, factor analysis, and projecting each variable’s total variation into a four-dimensional solution. The resulting manikin family is composed of 17 members, each with a different representation of body part proportionality. The validation techniques for the A-CADRE set involved an analysis as part of a helicopter cockpit redesign, where the A-CADRE results were shown to be equivalent to a sample of 400 aviators that were randomly selected for user testing. Therefore, the A-CADRE set can be applied to workstation design tasks using 34 manikins (one for each gender) and allows a user to represent a sample of 400 people. The A-CADRE set is presented as percentile values for 19 anthropometric variables as shown in Table 25.3.

TABLE 25.3 A-CADRE manikin percentile descriptions for 19 anthropometric variables (Bittner, 2000). A-CADRE manikins Measures

1

2

3

4

5

6

7

8

9

10

11

Stature

99.0

91.0

82.7

95.4

75.6

52.0

24.4

38.3

61.7

75.6

48.0

12 9.0

17.3

13

14 4.6

24.4

15

16 1.0

50.0

17

Illial chest ht.

98.7

84.8

90.4

96.0

63.4

45.1

28.6

20.1

79.9

71.4

54.9

15.2

9.6

4.0

36.6

1.3

50.0

Sitting height

96.6

89.7

31.5

91.0

93.6

85.0

21.6

83.2

16.8

78.4

15.0

10.3

68.5

9.0

6.4

3.4

50.0

Eye-height sitting

96.5

92.7

29.6

89.8

92.3

81.3

18.0

85.7

14.3

82.0

18.7

7.3

70.4

10.2

7.7

3.5

50.0

Popliteal height

98.1

67.5

91.8

97.3

64.4

59.0

37.7

10.6

89.4

62.3

41.0

32.5

8.2

2.7

35.6

1.9

50.0

Buttockeknee lth.

98.6

92.6

95.7

81.6

61.8

16.3

43.5

33.1

66.9

56.5

83.7

7.4

4.3

18.4

38.2

1.4

50.0

Shouldereelbow lth.

99.0

78.4

93.2

94.0

79.3

51.9

49.4

23.5

76.5

50.6

48.1

21.6

6.8

6.0

20.7

1.0

50.0

Forearmehand lth.

98.5

57.9

93.7

94.1

85.3

67.0

66.0

17.9

82.1

34.0

33.0

42.1

6.3

5.9

14.7

1.5

50.0

Bideltoid breadth

93.6

87.4

89.6

18.9

93.6

18.9

89.6

87.4

12.6

10.4

81.1

12.6

10.4

81.1

6.4

6.4

50.0

Hip breadth

96.9

96.1

89.6

34.4

86.4

12.3

69.2

84.1

15.9

30.8

87.7

3.9

10.4

65.6

13.6

3.1

50.0

Foot length

98.9

65.7

92.5

92.9

89.3

66.9

65.7

26.4

73.6

34.3

33.1

34.3

7.5

7.1

10.7

1.1

50.0

98.0

45.9

90.6

92.5

92.9

80.1

76.6

24.3

75.7

23.4

19.9

54.1

9.4

7.5

7.1

2.0

50.0

Functional reach

98.8

71.1

95.3

93.0

80.0

51.9

59.4

19.1

80.9

40.6

48.1

28.9

4.7

7.0

20.0

1.2

50.0

Funct. Leg throw

99.0

82.4

93.3

94.6

72.3

45.5

41.1

21.5

78.5

58.9

54.5

17.6

6.7

5.4

27.7

1.0

50.0

Acrom. Ht. sitting

97.1

93.4

35.8

88.9

92.2

77.3

20.1

84.9

15.1

79.9

22.7

6.6

64.2

11.1

7.8

2.9

50.0

Cervicale height

99.0

90.0

84.9

95.4

75.2

51.0

26.4

35.3

64.7

73.6

49.0

10.0

15.1

4.6

24.8

1.0

50.0

Tragon height

98.9

91.0

81.4

95.4

75.6

52.9

23.6

39.3

60.7

76.4

47.1

9.0

18.6

4.6

24.4

1.1

50.0

Weight

96.9

95.5

89.9

34.1

87.4

13.2

71.5

83.8

16.2

28.5

86.8

4.5

10.1

65.9

12.6

3.1

50.0

Biacromial breadth

97.1

76.0

88.3

45.6

97.8

50.0

90.4

79.4

20.6

9.6

50.0

24.0

11.7

54.4

2.2

2.9

50.0

Posture and anthropometry Chapter | 25

Hand length

343

344 PART | V Postural interactions

The advantage of the A-CADRE approach is that it clearly defines each of the evaluation population for any assessment. The proportionality of the human models can be interrogated, and accommodation issues can be easily identified with the makeup of the manikins that experience problems. The definitions of the manikins being independent of the data and any specific tool also allow them to be applied to the practitioner’s tool of choice and with nationally relevant data to the problem at hand. Other interesting examples include the Intelligent Moving Manikin (IMMA) software tool being developed by Chalmers University and the University of Skövde in Sweden (Högberg, Hanson, Bohlin, & Carlson, 2016). IMMA uses a further refined boundary manikin approach, recognizing the challenges faced by practitioners in generating families of human models for evaluation.

2.3.3 Encumbered anthropometry As highlighted earlier, anthropometry is traditionally collected from nude or seminude subjects. This allows landmarks on the human body to be easily identified. It also removes the inherent complexities of what to measure if the body is clothed. However, the majority of applications of anthropometric data are not situations where users would be unclothed. To make allowances for clothing, it is not uncommon to include simple adjustments, the inclusion of a 30 -mm heel on the foot to represent footwear, for example. However, the understanding of the impact of clothing on anthropometry and subsequently any human models constructed using these data is limited for the majority of applications. One area that has seen a more concerted effort to account for clothing and other encumbrance is the military. Military personnel are regularly required to wear various forms of equipment and body armor. This equipment effects the size and shape of any given individual, and this has the potential to significantly impact the design of equipment and vehicles that this population is required to interact with. A number of DHM systems have developed approaches to addressing the need to account for various forms of encumbrance. The US Army Research Laboratory has constructed a library of digitized military equipment to use with the Jack DHM system, part of the Siemens PLM suite of software (Hicks, Durbin, & Kozycki, 2010). RAMSIS is being developed to include a module called RAMSIS Defense that will include a library of clothing and equipment that can be added to the human form. Santos is a DHM system that has been developed in close collaboration with the US Army and has been recently developed to explore the optimization of load carriage by military personnel through their Enhanced Technologies for the Optimization of Warfighter Load (ETOWL) environment (Santos Human Inc., 2018). DHM tools provide considerable advantages to practitioners wishing to account for clothing and equipment. 3D geometry can be added to the human model to represent typically equipment such as footwear, helmets, respirators, backpacks, and other load carriage equipment. However, the majority of the available functionality is embedded with specialized add-ons typically aimed at military applications. Thus, the ability to include more common clothing or equipment considerations for less specialized applications, for example, an assembly line worker with or without gloves, is still a challenge. One of the main issues is the collection of the data itself and the understanding of the differences between the encumbered and the unencumbered anthropometry. Measurement techniques allow both to the captured; the difficulties lie in knowing where the human body is within the encumbered data. To aid in the collection of encumbered anthropometry, researchers have explored the use of so-called positioning aids that allow 3D body-scanned anthropometry to be collected from the same individual in a highly repeatable posture (Schwarz-Müller, Marshall, & Summerskill, 2018). This allows seminude data to be overlaid with various forms of clothing and encumbrance and for the data to be accurately positioned, giving a full understanding of the relationship between the human body and the encumbrance as shown in Fig. 25.7. This provides the means for anthropometry to be collected using existing technologies and databases to be constructed for common applications and ultimately to make the use of human models more representative across a variety of applications. Unfortunately, at present, these developments are still at the research phase and thus useable data are not yet available.

2.3.4 Interaction with external objects Clothing and equipment have the capacity to alter human anthropometric measurements. Anthropometric measurements can also be effectively altered through the interaction of the human body and its environment. The soft tissues of the body have the capacity to deform when interacting with objects; this may also be accompanied by a deformation of the object in question. Thus, in a seating situation, both the buttocks and thighs will deform as may the seat surface if not completely rigid. These interactions pose further challenges for those working with anthropometric data and DHM tools. Tools such as AnyBody (AnyBody Technology A/S, 2018) approach the construction of their human model differently to the majority of tools. As opposed to primarily being concerned with the outer form, AnyBody realistically models the internal structures

Posture and anthropometry Chapter | 25

345

FIGURE 25.7 Superimposition of 3D scan data, (A) seminude, (B) encumbered, (C) combined. Postures constrained by positioning aid to allow accurate determination of the differences between configurations (Schwarz-Müller et al., 2018).

of the human. Elsewhere researchers have explored specific situations such as grasping tasks (Gourret, Gourret, Thalmann, & Thalmann, 1989) or sitting (Al-Dirini, Reed, & Thewlis, 2015). These studies show that these data are challenging to obtain because typically what needs to be seen or measured is often obscured by the skin or the object being interacted with. In addition, the subsequent modeling is also challenging often requiring complex finite element model approaches that do not lend themselves to the more real-time DHM environment. Thus, even with these sophisticated tools and models, the ability to easily understand the effects of sitting on, grasping, leaning over, or otherwise interacting with the physical environment and what those interactions may do to the human form and ultimately to be able to represent what the human is actually capable of in any given situation is currently limited.

2.4 Anthropometry in user-centered design One of the main challenges in the field of anthropometry and its application in ergonomics and design and specifically DHM is the potential to lose focus on the ultimate aim. In what is essentially a human-centric endeavor, the numeric and reductionist approaches that have to be adopted make it all too easy to dehumanize the activity. The process of manipulating data to recreate fictitious humans from measures of individual limbs and parts of the human form seems somewhat contrary to a process that should have empathy with those who are being designed for. As has been shown, the data and the recommended processes suggest designing for 5th female to 95th male percentiles. This advocates the designing out of the top 2.5 and bottom 2.5% of the population. This is also the adult, able-bodied population, potentially based on data from a nonrepresentative group such as a different nationality or from military personnel used for a civilian application. This starting point specifically begins with an aim that is not fully inclusive, and as has been shown, the subsequent process is only likely to further reduce the accommodation of the desired population. It is all too easy to become focused on the numbers and not the people. The standardized form of most of the available data sources also leads to an unrealistic understanding of the human diversity within a given population. If the data available have been collected 20 years ago, from adults (18e65 yrs), from people without disabilities, then they are going to be unrepresentative of large proportions of the population. The increasing BMI of many global populations has led to significant changes in body shape (Bridger, Brasher, & Bennett, 2013). The majority of human modeling systems represent an increase in the BMI in a very proportional manner, the weight being distributed evenly about the body, and yet people carry their weight very differently and hence why the colloquial terms of “apple” and “pear: are used to visually categorize these shape differences (Muir & Rush, 2013). For the informed, none of these issues and arguments are new; however, one of the disadvantages of more easily available and useable software tools is the ability for them to be applied by practitioners who lack this detailed understanding. None of the issues presented in the use of anthropometric data are trivial, none of the research developments, or existing software tools are so sufficiently robust and automated to remove the need for ergonomics expertise. It is thus critical that all applications of these tools and data are done in full light of the issues discussed.

346 PART | V Postural interactions

Alternative approaches from the traditional norms have also been explored. Rather than try to recreate individuals from body part measures the Human Anthropometric Data Requirements and Analysis (HADRIAN) tool is an attempt to create what could be considered a virtual user group (Marshall et al., 2010). In this case, more than 100 people have had their anthropometric data captured, but rather than them being dismembered to form tables of stature, arm length, and so on, the data have been maintained as individual data sets. This allows individuals to be recreated with complete validity and used in evaluations. In addition, these individual human models also have represented joint mobility and capability data. The group also contains a significant proportion of people who are older and disabled, addressing the shortcomings highlighted with the majority of current anthropometric data. The data are also presented with an image of the person together with information on age, gender, nationality, occupation, etc. as shown in Fig. 25.8. This approach attempts to address not only the concerns with how to define the assessment population but also the dehumanization of anthropometric measures. Designing out one of the people in the virtual user group is placed into context by being able to see the person excluded; these are no longer faceless virtual constructs. The HADRIAN approach is itself not without its limitations; there is still the matter of which human models to use in any given evaluation. Ideally, the whole group would be evaluated for all assessments; however, 100 human models form a very large assessment group. Even with task-based automation, the processing time can become unwieldy. In many cases, a subset of the whole group could be used, but again, this comes down to the expertise of the practitioner in being able to make informed critical choices.

FIGURE 25.8 HADRIAN database interface showing the layout based around individuals within the group and a sample of data.

Posture and anthropometry Chapter | 25

347

2.5 Anthropometry and its relationship with other key measures Anthropometry may be the basis for all human models, but anthropometric data are inherently tied to posture. The posture of a human or human model essentially changes an anthropometric measurement. This is one reason why anthropometric measurements are so standardized; this ensures posture does not unduly change the data being collected. Once a human model has been constructed within a DHM system, the human model can then be postured to explore specific key situations or define critical dimensions. The posture will be a function of the mobility of the human, via a model of joint mobility and the task being performed. Therefore, anthropometry forms only one aspect of the DHM process. DHMs go well beyond static templates such as Jack and Jill (Dreyfuss, 1967); the ability to model dynamic activities and postures allows much more comprehensive applications. For any given application, there are a number of critical pieces of data that need to be understood.

2.6 Recommendations for the use of anthropometric data in human modeling It is acknowledged that the previous discussions paint a challenging picture for practitioners wishing to use anthropometric data, particularly in the field of human modeling. It is important that practitioners are aware of these issues and once aware that they do not become overwhelmed by the complexities. There are many thousands of examples of robust applications of human modeling to real-world problems. What follows is a recommended list of considerations and ways to move forward in any use of anthropometric data. l

l

l l

l

l

Make sure there is a full understanding of what is trying to be achieved in the use of anthropometric data. This understanding must be shared by all stakeholders, and there must be a clear context. l This includes an understanding of the type of applications to which it is realistic to apply DHM tools which can be derived from the literature. Clients can sometimes expect DHM systems to perform beyond any realistic expectation. Make sure the source of the anthropometric data being used is fully understood. l Question the applicability of the data to the given situation; consider when were the data collected; what nationality, gender, age, ability, and occupation is the sample; how large was the sample (n); are all the required data present; have the data been manipulated, have some data been synthesized? This analysis should aim to identify instances where additional data should be sought to support the sample definition. Ensure stakeholders are aware of Pheasant’s fallacies (Pheasant & Haslegrave, 2006). Consider collecting a custom data set to support the application. Certain applications cannot rely on existing data; in some cases, it is important to gather new data. l In some instances, exiting data may be representative, but some modest data collection can be used to compare a specific population to national data to check this assumption l When collecting data, aim for a distributed approach, look to obtain data for each percentile decile 0e10, 11e20 etc. for each critical dimension. For all their limitations, the extremes of the population are typically the most informative. Where the number of variables (e.g., adjustable features) is low, start with univariate measures/human models. l While the recommendations may suggest starting 5th female to 95th male, where possible, first female to 99th male is recommended. l Be sure to factor in the nationality of the data, for example, first to 99th US data are not likely to reveal much for a Japanese population as anthropometry are markedly different. Wherever possible, use a boundary and/or distributed manikin approach. This may be built into the DHM system. If not, it is recommended that an approach such as A-CADRE is applied. l Regardless of the way in which the specific human models are identified, it is important that due consideration is given to the key design variables because this will inform the most appropriate manikins to use. l Be critical about what manikins are identified and understand what each one is likely to reveal about a given evaluation. For example, in a car driver packaging exercise, a largely univariate manikin will have proportional body dimensions. Thus, the length of the legs and the positioning of the seat are offset by a proportional trunk length and arm length for reach to the steering wheel and head clearance. However, a manikin with long legs, long trunk, and short arms will have to have the seat further rearward, their long trunk may decrease head clearance and so their posture may be reclined; in this instance, the short arms may prove to be problematic for the reach to the steering wheel. l If the manikin family is prohibitively large, then use the key design variables to selectively target specific manikins with specific body proportionality: long legs, long bodyeshort arms; short legs, long bodyeshort arms, etc.

348 PART | V Postural interactions

l

l

l

l

Do not only consider body size (lengths) but also be sure to consider body shape. For example, many global populations are identifying issues with obesity; thus, the body shape of any human model family in an evaluation must consider extremes of corpulence if it is to be representative of these populations. Make adjustments for clothing and equipment. This can often be done within a given DHM tool, either through built-in functionality or through the addition of simple geometry to represent the encumbrance. Be clear about any limitations with a given analysis to avoid the results being misinterpreted or conclusions being drawn that are not substantiated. Ensure that the stakeholders and operators of the DHM system being used are aware of the number of anthropometric dimensions that are being used to build each manikin. Many DHM systems now present very realistic manikins. If the DHM is defined by a handful of anthropometric measures, it is possible to assume that a specific task that is being analyzed, such as clearance to an object from the leg or forearm, is being accurately modeled, whereas that clearance may only be represented by an approximation for a leg circumference or arm circumference in certain cases.

3. Conclusion Human anthropometric variability is extremely diverse. The use of these data to support DHM can therefore be a challenging prospect. The simplification of this diversity by the literature and other recommended practice and the accommodation of DHM software tools for univariate human models poses significant challenges for the uninitiated. This chapter discusses a range of issues that users of anthropometric data should be aware of. Many of these issues are situation dependent and often have “no right answer.” Practitioners should be aware of the issues and make use of best practice in their evaluations. DHM is a powerful tool, and it is critical that proponents and more casual users of such technologies recognize the limitations, exploit the most appropriate data available, and avoid misrepresentation of any results. Used judicially, DHM has the potential to model humaneproduct/workstation/workplace interactions early on in the design process and to ensure ergonomics issues are considered up-front, and anthropometric diversity of users is given appropriate consideration.

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350 PART | V Postural interactions

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