What are various measurements of the human body, including height and weight, called?

Abstract

Anthropometric data are data on human body size and shape and are the basis upon which all digital human models are constructed. All aspects of the utility of a human model are therefore governed by its anthropometric characteristics. The physical size, the postures that can be adopted, and the tasks that can be performed are all influenced by some degree by anthropometry. This chapter explores anthropometry from how anthropometric data are presented, how they can be used to define an individual or a population, and how they can be strategically used to inform ergonomics evaluations using human models. The chapter also discusses the caveats often associated with anthropometric data sources and the traditional approaches to their use. The chapter also highlights some of the key methods in the field of anthropometry and how they can be used to create human model families to support practitioners in their use of digital human modeling.

2.2.3 Anthropometric data

The anthropometric data was extracted from the 3D scanned data. First the foot was aligned according to the foot centerline (Luximon, 2001). The Foot measures are presented for Left (L) and Right foot (R) separately. Foot Length (FL) is the maximum length from heel to toe. FWx where x = 20, 50, and 80 are the width of the foot at 20%, 50%, and 80% of foot length from the heel. This represent the foot width at the rear foot, mid foot, and forefoot regions. FHx where 50 and 80 is the height of the foot at 50% and 80% of foot length from the heel. This represent the foot height at the mid foot (instep) and forefoot regions. FGx where 50 and 80 are the foot girth of the foot at 50% and 80% of foot length from the heel. This represent the foot girth at the mid foot (instep) and forefoot regions. Fx where x = 50 and 80 and Fx80 are the foot flare in degrees as shown in Fig. 2.1. This is important for footwear design as it provide information of the curvature of the outsole. The anthropometric data together with mean standard deviation and percentile (per) values are shown in Tables 2.1–2.6.

Table 2.1. Anthropometric measures for Male Children.

Table 2.2. Anthropometric measures for Female Children.

Table 2.3. Anthropometric measures for Male Adult.

Table 2.4. Anthropometric measures for Female Adult.

Table 2.5. Anthropometric measures for Male Elderly.

Table 2.6. Anthropometric measures for Female Elderly.

6.3.2 Fit mapping

When anthropometric data are available, the data should be matched with the product under design or evaluation. This analysis, also called fit mapping, indicates whether clothing sizes are accommodating the user population. Two examples of fit mapping will be given: one in which anthropometric data were available and one in which measurements had to be taken to obtain the necessary information.

The first example deals with the trousers of the Dutch armed forces. Nine sizes were available based on the NATO sizing system (Standard NATO Agreement STANAG2335). The sizes are based on waist circumference and inner leg length. We calculated what percentage of the user population would fit in each size. The results are shown in Fig. 6.4.

The NATO size system is indicated by four numbers for the length range, followed by four numbers for the circumference range. For instance, 8595/7080 indicates the upper left size in Fig. 6.4. 8595 stands for an inner leg length of between 85 and 95 cm, and 7080 indicates that the waist circumference lies between 70 and 80 cm.

The increasing tendency to be overweight in The Netherlands can be expressed by the secular trend in body weight, which is about 0.3 kg/year, and waist circumference. Therefore, the sizes with small waist circumference became obsolete. We decided to eliminate sizes 7585/7080, 8595/7080 and 9000/8090 and to introduce a new size that was not available before: 7080/0010. This size was accommodating 5.9% of the user population.

The net result was that the number of sizes was reduced from nine to seven and the accommodated user population increased from 87.5% to 91.1%. The few subjects with small waist circumferences now have to wear a slightly wider pair of trousers.

Sometimes the term efficiency is used in this context. The sizing efficiency is defined as the average percentage of the population covered by a size. In this example the efficiency increased from 9.7% to 13.0%. Of course, this term should be interpreted in combination with the percentage of the user population that is covered by the sizing system. Figure 6.5 shows the relation between the number of sizes and the percentage of the accommodated population. This graph shows that it may be appropriate in some circumstances to use only six sizes: 90.2% of the population is accommodated with an efficiency of 15%.

The second example started with a question from the Belgian company Bivolino (www.bivolino.com). This company sells men’s shirts over the Internet and wanted to extend their business to female blouses. Although this example is from the ready-to-wear industry, the methods used are applicable for providing fit for functional apparel. For the men’s shirts, Bivolino created an effective patented system in which only four questions (age, stature, body weight and collar size) formed the starting point of the production chain. It took them a while to fine-tune the system, based on returns due to improper fit. Now, the question posed to TNO was whether this process could be speeded up based on fit mapping. First, 50 females, representative of the user population, were invited to the laboratory, measured accurately and fitted the blouses. The size of the blouse that fitted best was recorded, as well as the changes in patterns that should be made to arrive at a perfect fit. Regression analysis showed that the best fitting size could be predicted rather well with age, stature, body weight and bra size as indicators (Fig. 6.6). In 55% of the cases the size was predicted correctly and in 96% the size was predicted plus or minus one size. Not only was size predicted using regression analysis, but also the individual measurements were assessed. Arm length for instance is relatively well related to stature. Now, for every female ordering a blouse through the Internet, the size is predicted and adjustments are made according to the blouse of this size to the estimated individual sizes. The experience with the male shirts show that the number of returns is very low using this method; for female blouses it is too early to draw conclusions.

Key Points

●

Correctly selected anthropometric data assists in the design of work areas and workspaces to support neutral working postures and improve productivity. This helps to reduce error and injury.

The principles of positioning should be applied to the functional arrangement of workspaces and work areas.

The type of workspace will be dependent on the tasks to be undertaken including seated, standing, or sit/stand workspaces. These are equally relevant to plant side areas.

For seated workstations, the type of seating provided should be carefully chosen relative to the tasks undertaken and with user input.

The positioning of equipment needs to enable good viewing postures.

Vehicles present some specific considerations.

2.3.2 Anthropometry: Body Size

Anthropometrists, measurers of the human body, have collected body size data for many years. For example, the measuring units of foot and hand have been derived from the dimensions of body parts. The term anthropometry is derived from two Greek words: antropo(s), or human, and metricos, or measurement. Anthropometry is used extensively by ergonomists to design tools, equipment, plants, manufacturing lines, clothes, shoes, and the like to ensure the proper fit to the person. Therefore, to achieve proper fit, it is important to have details on the dimensions of the appropriate body part. For example, the size of the hand is used to design the dimensions of controls such as switches and push-buttons, while details of arm reach are necessary to position controls at appropriate distances.

Two primary categories of anthropometric data interest ergonomists:

Structural anthropometry, also referred to as static anthropometry or static dimensions. These are measurements with the body in a still or fixed position; for example, stature or height, weight, head circumference.

Functional anthropometry, also referred to as dynamic anthropometry or dynamic dimensions. These are measured with the body engaged in various work postures, indicating the ranges of motion of individual body segments; for example, arm reach.

It is important to point out that static anthropometry data are often measured on unclothed (nude) individuals, mainly to ensure consistent results. Therefore, corrections must be made to account for increases in body size due to clothing, such as a process operator working outside during an Alaskan winter and another during a Texas summer. In addition, allowances must be made for wearing safety shoes and hard hats, which could add about 10–12 cm (4–5 in.) to the stature that must be considered in the design.

2.3.2.1 Sources of Body Size Variability

The various genetic, biological, and physiological differences between humans influence the way they vary with respect to body dimensions in terms of height, weight, shape, and the like. This can be noticed if you observe people in a shopping mall. Therefore, we need to be very careful using anthropometric data if they are to be of value (Pheasant, 1982a, 1982b).

The common practice in ergonomics is to specify anthropometric data in terms of percentiles. A percentile refers to a percentage of the population with a body dimension up to a certain size or smaller. For example, if a 95th percentile height (stature) is 170 cm (66.9 in.), it indicates that 95% of the population have heights up to 170 cm. Or, 95% of the population have heights of 170 cm or less and 5% are taller than 170 cm. If, on the other hand, the 5th percentile stature is 150 cm (59 in.), it indicates that 5% of the population are shorter than 150 cm and 95% are taller.

When a particular design (i.e., placing a valve hand wheel at a given height) is expected to be used by many people, we need to consider the following variables in influencing body size and adjust the design accordingly:

•

Gender. Men are generally larger than women at most percentiles and body dimensions. The extent of the difference varies from one dimension to another. For example, hand dimensions of men are larger than women sizes—hand finger thickness of men is about 20% larger, while fingers are about 10% longer for men than for women (Garrett, 1971). Women, in general, exceed men in five dimensions: chest depth, hip breadth and circumference, thigh circumference, and skin-fold thickness. O’Brien (1985) proposes a complete list of anthropometric differences between men and women.

•

Age. Body dimensions generally increase from birth to early twenties, remain constant to around age 40, and decline afterward into old age as part of the normal aging process. For example, stature or height reaches full growth at around age 20 for males and 17 for females (Trotter and Gleser, 1951; Damon, Stoudt, and McFarland, 1971; Roche and Davila, 1972; Stoudt, 1981). Decline in stature is more pronounced in women than in men. Therefore, it is important to define the user population early in the design cycle.

•

Nationality and culture. Nationalities and cultures differ in body sizes. For instance, Asians tend to be somewhat shorter on average than Northern Americans, while certain cultures from southern Sudan (Africa) tend to be taller. Therefore, it is important for the designer to define and use the anthropometry data related to the user population nationality and culture. Table 2-1 (and Figure 2-5) presents an example of anthropometric values for three different nationalities: North American, Japanese, and Hong Kong.

Table 2-1. Selected Anthropometry Data for Different Nationalities in Centimeters (cm) and Inches (in.)

	North Americans	Japanese	Hong Kong
	95th% Man	5th% Woman	95th% Man	5th% Woman	95th% Man	5th% Woman
Anthropometric Dimensions	(cm)	(in.)	(cm)	(in.)	(cm)	(in.)	(cm)	(in.)	(cm)	(in.)	(cm)	(in.)
A. Vertical grip/reach	217.5	85.7	177.8	70.0	207.5	81.7	168.0	66.1	210.5	82.9	168.5	66.3
B. Stature/head height	184.4	72.6	149.5	58.9	175.0	68.9	145.0	57.1	177.5	69.9	145.5	57.3
C. Shoulder height	152.4	60.0	121.1	47.7	143.0	56.3	107.5	42.3	146.0	57.5	118.0	46.5
D. Elbow height	119.0	46.9	93.7	36.9	110.5	43.5	89.5	35.2	108.0	42.5	87.0	34.3
E. Eye height	172.7	68.0	138.2	54.4	163.5	64.4	135.0	53.1	164.0	64.6	133.0	52.4
F. Forward grip/reach	88.3	34.8	64.0	25.2	75.0	29.5	57.0	22.4	77.0	30.3	58.0	22.8
G. Knuckle height	80.5	31.7	64.3	25.3	80.5	31.7	65.0	25.6	81.5	32.1	65.0	25.6
H. Knee height	59.2	23.3	45.2	17.8	53.0	20.9	42.0	16.5	54.0	21.3	41.0	16.1
I. Waist height	110.5	43.5	86.4	34.0	89.5	35.2	70.0	27.6	92.0	36.2	71.5	28.1

Note: Add about 4 cm (1.5 in.) for shoes and 7.5–10 cm (3–4 in.) for hard hat.

Figure 2-5. Anthropometry figure to guide Table 2-1.

•

Occupation. Differences in body size dimensions among occupational groups is common and well documented. For example, manual workers, on the average, have larger body sizes than sedentary workers. Sanders (1977) found truck drivers to be taller and heavier than the general civilian population. This difference among occupations may be the result of

1.

Diet.

2.

Exercise.

3.

Physical activities imposed by the job (i.e., manual handling).

4.

Imposed selection, such as individuals need to be a certain height to be accepted in a particular job.

5.

Self-selection, such as individuals with a given height choose a particular job for practical or sociological reasons.

We must take great care of not using anthropometric data obtained from groups of one occupation, such as armed forces, to design the environment of another, such as office workers.

•

Historical trends. The average size of people has been increasing over the years. For instance, the average adult height in Western Europe and the United States increased about 1 cm (0.4 in.) per decade (Sanders and McCormick, 1993). This is so, perhaps, because of better diet, medical care, hygiene, and living conditions. As designers, we need to consider present-day users as well as future generations to ensure proper systems design few decades down the road.

•

Body position. Posture affects body size. For example, restraints such as seat belts, affect data applicability of forward reach.

•

Clothing. As mentioned earlier, almost all anthropometric data are obtained from nude individuals. Therefore, the type (material) and amount of clothing add to body size and can also create restriction of movement such as affecting overhead and forward reach. Another example is the use of gloves where allowance must be made to accommodate different thicknesses of gloves.

4.2 Body shape and size in children and teenagers

In general, the anthropometric data obtained for this study to understand the general overview of the variations of body sizes, body variations, and body proportions of the children ages 7–17 in one country. The body dimensions can vary in terms of sizes, shapes, and proportions [1]. So far, the anthropometric data have been extensively collected and studied for many different purposes like ergonomics, medical, health, designs, and nutrition and also for the development of sizing systems for apparel manufacturing. These data provide a wealth of information about body dimensions, which is useful for the development of sizing systems for clothing [2].

As early as 1827, studies found that form and variation are the two components by which to classify the human body [3,4]. Later, in 1932, Huxley’s [5] research on body variations showed that numerical body dimensions can describe body shapes. In addition, Lele and Richster [6] also mentioned that the relationship between the important body dimensions such as hip, bust, and waist can be interpreted as indicators of shape differences. Many years later, another study reaffirmed the fact that the human body can be classified in terms of body shapes [7]. Interestingly, body shapes have been investigated for many reasons, including health, physiology, understanding of physical aspects, perceptions of attractiveness, and certainly body image and clothing fit [8,9].

The significance of body shape is also beginning to be recognized in the apparel industry for the development of sizing systems [10–12]. This is supported by Gupta [13] who stated that body shape analysis is necessary for the development of accurate apparel sizing. In addition, several studies have revealed that sizing systems must be based on body size and shape and not on age to provide a good fit [14–18]. Hence, manufacturers must ensure they meet the demand of consumers by delivering clothes that have a good fit, are the right size for different body sizes and shapes, are pleasing to the eye, and catch the attention of consumers [19].

Some of these issues are related to body self-perception of children and adolescents; as discussed in the following section, it has been proven that if children are dissatisfied with their body shape, they will likely have problems with their clothing fit [20]. There is also evidence that most obese or plus-sized children are not satisfied with their body shapes and often find it difficult to find the right-sized clothing [21].

Many studies suggest that the establishment of preferred body shapes begins in early childhood [7,22–24]. Previous research discovered that not only are adults conscious of their body image and shape but also children are [20,25,26]. Findings show that adolescents have a fear of being overweight and experience body shape dissatisfaction [18,27]. In another study, results revealed that children as young as age 5 show a desire to be thin with a tubular body shape and perceive thinness as the “right” body shape. Sorensen et al. [28] asked young children their preferences of the ideal body shape, and they chose the underweight figures. When the same question was posed to teenagers, their preference changed from underweight to the average weight figures.

There is also evidence that body image is of special concern for adolescents, not only in females but also in males [29,30]. Chen et al. [20] claim that many adolescents desire to be thin but that there are distinct differences between genders. The findings are consistent with other studies conducted in both Western and Eastern cultures, in which girls show more concern about their body shape and are more dissatisfied with their body shape [31]. While girls showed an inclination toward wanting to be thin, boys showed the opposite desire, associating being larger with being muscular and having physical prowess [32,33]. Girls generally want to be smaller. Boys may want to be thin, but they also want to increase muscle mass [28]. Just as females see an ideal figure in the hourglass shape, males in the United States want to achieve a trapezoid shape [33].

It is essential to understand that previous research has shown that at a very early age, children are already sensitive toward the ideal body shape [34]. This means that they want to wear clothes that can enhance their body shapes as proven in the study by LaBat and De Long [35], who found that when clothing does not fit, the consumer may perceive the cause as related to their body and not the clothing. Another study highlighted that adolescents as early as 9–15 years old go through physical changes that occur in different phases and at different rates. These physical changes affect the fit of clothes [36].

However, Salusso [37] suggested that rather than concluding people’s bodies are disproportioned, it is time to seek validity in sizing proportions. Many others have reported that the rapid growth of adolescents not only results in sudden physical changes but also affects their self-perception and social psychology [38]. Social psychology and physical appearance in children are very much related; there is evidence from the literature that children may feel dissatisfied with their body image, which is actually a mental picture of their body rather than their real body shape. Therefore, the ultimate appearance and the fit of clothing must not only be comfortable but must also meet the expectation of body image that pleases the wearer [36]. In addition, with body image strongly connected to body size and shape, it is important that the sizing is based on actual body size and shape, not on age, in order for a garment to fit well [39].

The body sizes showed that there is a difference of height and weight within different genders. From ages 11–12 toward the adolescent years (puberty), the female mean height is higher than the male mean height. Females enter puberty between ages 8 to 13 [40,41]. This is In contrast, males start to have their major growth spurt from ages 10 to 15 years old [42,43]. This phenomenon is associated with puberty ending and sexual maturity in males, which eventually slows down as height is relatively the same from 15 to 17 [44,45]. This is probably due to the fact that the female children reached puberty earlier than age 15 and therefore have stopped growing steadily in terms of height [46,47]. Boys continue to grow until age 16 or 17 as their puberty age stops around those ages. Thus, there is a clear trend from the findings of the research that there is steady growth in height for male samples all the way from age 7 until 17. As compared to females, the steady growth in height occurs from age 7 to 15 and then there is a very slow growth from age 15 to 17 as age increases. The finding also shows that female’s growth of horizontal body dimensions from age 7 to 12 is rapid and steady.

4.2.3 Variations in practice regarding sizing systems, size codes and ease allowances

Although the usefulness of anthropometric data in developing effective sizing systems has been recognised for many years, its utilisation has neither been uniform nor a panacea for all problems. Traditional approaches to anthropometry have prescribed methods and instruments for measuring linear heights, depths and widths, sometimes with calibration. Usually landmarks are identified using bone structures beneath the skin which are then marked on the body. Although landmarks show the beginning and end of a dimension, their determination and measurement can be inaccurate and variable, resulting in invalid and unreliable data. To avoid intra- and inter-measurer errors, training of personnel is necessary. Sampling procedures have to consider population representation, especially where size charts are a required outcome. Analysis and interpretation of data require statistical knowledge and its application to clothing design issues. Because surveys are time consuming and expensive, availability of reliable data that are current is rare; and when available they are proprietary and not freely available in the public domain. This has sometimes resulted in the use of outdated sizing systems.

Fitting trials have been used to establish the suitability of products for the user population (Pheasant, 1986). These are experimental studies in which the designer systematically explores user preferences by means of adjustable mock-up of the product using dummies, live models or, more recently, virtual three-dimensional models. Accuracy of fit is a goal that many current technologies stress (DOB-Verband, 1994; Roebuck, 1995). As opposed to custom-made garments where various dimensions of the body are directly transposed to the relative parts of the pattern resulting in the finished garment fitting the body, the ready-to-wear garments are made to fit an imaginary average size or categories of people whose measurements and figure characteristics are not known before manufacturing begins (Carr and Pomeroy, 1992). Since the advent of mass production, nomenclature – i.e. what to call a size – has been a problem and this persists today. Although communication of fit is important in the clothing industry, confusion exists and there is non-uniformity of symbols or codes used for size designation. With a lack of industry standards on fit, there has been size coding variation within and between retailers and manufacturers, with sizing sometimes being arbitrary. For example, some numerical sizes do not correlate to any body measurements and some retailers have utilised vanity sizing. Garments are labelled variously and sometimes inconsistently with different codes: numerical symbols, e.g. 12, 14, 16 or 1, 2, 3, 4 or 34, 36, 38; control body measurement, e.g. To fit chest 32, hip 38, neck 38.5 cm; finished garment measurement, e.g. inside leg 31; height, e.g. To fit height 92 cm; figure type, consisting of XXS, XS, S, M, L, XL, XXL or Short, Medium, Tall; age and weight, e.g. Age 2 years or Age 2–3 years; 7 lb. In the USA, children’s sizing utilises odd numbers 3–13 and Misses sizes have 4–20 (Tamburrino, 1992). In Germany, the codes for girls’ size ranges are SS for extra slim, S for slim, N for normal and EW for chubby (Cooklin, 1991; DOB-Verband, 1993). In the UK, children’s size codes utilise height and age. In practice, actual garment sizes differ owing to manufacturers’ preferences depending on style, and size codes are variously based on numbers, figure type and key or control dimensions.

Before making garments from the size charts, ease allowances are added to certain dimensions. These allowances vary between manufacturers who make similar garments and this is due to the fact that manufacturers have freedom in deciding these ease allowances. Intervals are usually selected for grading and these vary between manufacturers (Kemsley, 1957, BSI, 1990; Workman, 1991). Proper fit of apparel depends on the relationship between the size of the garment and that of the wearer, and this value is critical in protective clothing. Consumers have varying preferences regarding fit, style and design.

Anthropometric data (EN 547-3)

The technical standard presents the anthropometric data required to determine the measures and sizes of workplaces. These data are based on information collected from representative population groups in Europe, covering about three million individuals. The parameters assessed included both genders.

Table 13.9 includes the definition of the parameter, its description, together with a picture and the corresponding value for the anthropometric measurements used in the standards.

Table 13.9. Anthropometric data at the 5th and 95th percentile – CEN data

The column referring to the 5th percentile contains values referring to the 5th percentile for the anthropometric measurements of the European population; likewise the column referring to the 95th percentile. Table 13.9 shows the values to be used in the design phase, which may either be those of the 5th or 95th percentiles, depending upon the situations. Sometimes, they may both be necessary.

5.5.3 Horizontal grading assumptions

Assumption 5. The horizontal components of front and back grade rules for the bodice are identical.

This assumption appears to be contrary to anthropometric data. Early US and British sizing standards do not divide increases in horizontal measurements equally between front and back. The 1958 and 1970 US sizing standards (National Bureau of Standards, 1958, 1970) and the National Joint Clothing Council of Great Britain (1957) specify a front increase that is greater than the back at the bust level, and a greater increase for the cross-back than for the cross-chest. The two early US sizing standards (National Bureau of Standards, 1958, 1970) specify a front increase that is greater than the back at the waist, and a back increase that is greater than the front at the hip. Cooklin (1990) averaged measurement data from four national anthropometric surveys and recommended a front bust increase that is 62.5% of the total bust increase. Cooklin (1990) and Taylor and Shoben (1990) also used grade rules with a greater increase at the cross-chest than at the cross-back. These examples are the exception; most grading sources use identical horizontal components for front and back grade rules for the armscye and side seam. This assumption has been tied to simplified grading systems.

To test Assumption 5, arc measurements are needed but were not available from the 1988 anthropometric survey data. As an alternative method, below-bust circumference was used to approximate chest size. If below- bust and bust circumference increase at the same rate as size increases, then the front bust arc is not increasing at a greater rate than the back bust arc. The regression results establish that the below-bust circumference increases at only 68% of the bust increase for every size up to bust 39 inches (99 cm) and 85% for larger sizes. Waist grade rules could not be tested without access to arc measurements in the anthropometric data.

Assumption 6. Shoulder width rule – the horizontal increase for the front and back shoulder and armscye points is proportional to (generally one half of) the bust increase. These grade rules are relative.

Price and Zamkoff (1996, p. 28) described the relationship of the shoulder increase to the bust increase as ‘the cross-shoulder grade of the bodice is always one-half of the cross-bust grade’. In the example used in Fig. 5.5(b), a bodice graded with the 4 cm grade, following US grading practice, will have a 2 cm increase across the front and back at the bust. The increase at the cross-shoulder, cross-chest and cross-back widths is half that amount, 1 cm (and the grade rules are 0.5 cm for half the pattern piece). British sources do not use horizontal grade rules that match front to back and the cross-chest increase does not match the shoulder increase (see Fig. 5.5(a)). However, all sources use relative horizontal grade rules for the shoulder points and sleeve notches.

The skeletal structure of an individual for the most part determines the width of the shoulder and armscye. However, the soft tissue covering the ribcage and the breasts are added to the size of the ribcage to determine the bust circumference. It is doubtful that the wider range of bust circumferences in the population can be directly proportional to the limited range of shoulder widths.

The three available anthropometric measurements are shoulder breadth, cross-back width at midscye, and cross-back width at scye. (There are no front measurements in the anthropometric data to test.) The regression results diverge from grading practice as the location of the measurement moves up from the bust level. The increase for cross-back width at scye measurement is very close to the expected 50% of the cross-bust increase. The increase for cross-back width (at midscye) is only 40%, while the increase at shoulder breadth is 28% for sizes up to a bust of 34 inches (86 cm) and 16% for larger sizes. The resulting grade rule for shoulder breadth follows the pattern for an incremental and not a relative grade rule.

2.3.3 Static or structural anthropometry

This is the traditional and simplest method of collecting anthropometric data. Body circumferences, lengths of body parts and volumetric measurements are recorded while the body is held still in fixed, standard positions. Tools and methods for collecting these measures have been discussed in an exhaustive review by Bye et al. (2006) who have classified them under:

Linear measures which yield data in the form of distance between two points. Figures 2.2–2.5 show some typical linear measures collected for design of clothing.

Multiprobe methods which use a combination of linear methods with other tools to map the body’s contours.

Body form methods which give information about the surface, shape and volume of the body.

Standard measures are shown in Figs 2.2–2.5 and are classified below.

Body form methods

In certain applications, such as design of protective equipment or clothing, an efficient product-user fit can be best obtained only when a complete 3D profile of representative subjects is available from the target population. Several techniques have been used to obtain data of the body in 3D. The first such method was ‘draping’ where the fabric was draped directly on the body to capture the form in 3D. Currently, dress forms or mannequins are used for the same purpose. Dress forms moulded directly from the scans of fit models can also be obtained by manufacturers. With advances in 3D scanning technologies, additional data about the body such as volume, surface area and curvature of the body can be extracted using shape quantification technologies. The technology has matured in the last decade and a variety of software is now available for translating these data and for extracting point, line, surface, shape and volume data from the scans. This is the most accurate method of taking measurements, besides being faster and less invasive than the earlier methods.

There are certain limitations in use of 3D methods for body measurement, which must be known to a designer. The first relates to the fact that all data obtained from a 3D scanner are not necessarily 3D in nature. In some earlier studies, 3D scans were used to extract 1D data, similar to the linear measures obtained from traditional anthropometry, either because of the limitations of technology or due to a lack of awareness about the potential of technology. Even when the data are available in 3D format, like the digital 3D point cloud models, they cannot be used directly by designers. The presence of a large number of measured points and a lack of consistent representations between various scans present challenges in their use. Progress in the field of 3D image analysis techniques, during the last two decades, has made it possible to record and access high resolution 3D scans with a large amount of detailed geometric information about the human body shape. Azouz et al. (2006) applied principal component analysis to determine the variations in height, weight, posture and muscularity between bodies so as to bring all models into correspondence with each other. Large scale scan databases have been created through projects such as Civilian American and European Surface Anthropometry Resource (CAESAR), Size UK, Size USA and National Institute of Occupational Safety and Health (NIOSH) head and face database (Robinette et al. (1999)).

It is now possible to obtain realistic 3D models of human bodies, faces or heads representative of the generic shapes present in a given population (Blanz and Vetter, 1999; Bradtmiller and Freiss, 2004; Haar and Veltkamp, 2008). Luximon et al. (2011) used 3D point cloud data from the scanned heads of Chinese people to create highly detailed and accurate Chinese heads and faces with complete anatomical features and textures. The research output has been commercialised and it is possible to purchase, (off the net) a series of head shapes of the Chinese population from Certiform.org. Heads of six males and six females representing the 5th, 50th and 95th percentiles of the Chinese population are available on a DVD in several formats such as IGES (surface) and VRML (mesh) that are compatible with most CAD systems (Fig. 2.6). Corresponding physical models of the heads are also available. Availability of 3D head data in this form has opened up a whole lot of opportunities for designers to design a variety of eyewear, facewear and headwear for the Chinese population with very high precision and accuracy (Fig. 2.7). It is expected that in future, more and more organizations will be moving towards establishing 3D digital human databases of their target population and using them directly for their design problems.

2.6. 3D anthropometric models of Chinese males and females at 5th, 50th and 95th percentile.

Image courtesy: http://www.sizechina.com/products.php.

2.7. 3D scan of a head being used as a reference to design eyewear and an audio headset.

Image courtesy: http://www.sizechina.com/designlab_headset.php.