• The spherical shape makes repeatable, consistent alignment challenging
• There are few reliable landmarks on the cranial portion of the head
• Hair obscures the head surface
• Hair types and styles are hugely variable
• All five human senses (sight, sound, taste, smell, and touch) are on the head

As a result, until there is a prototype product to test, we cannot reliably establish where the product will fall with respect to the head surface or the head and face features such as eyes, and ears. In this article we review some of the challenges and suggest how to deal with them.

Lack of Reliable Generic (Not Product Specific) Head Orientation

For most products there is no reliable generic or universal head orientation that is effective for product development. In the technical report Maximizing Anthropometric Accommodation and Protection (Robinette 2007) three orientations were examined for 747 US navy personnel: 1) the Principal Axis System (PrinAx), 2) an approximate corneal plane alignment (Eye) and 3) a top-of-head alignment (TopHead). Figure 1 shows 10 subjects in each of them. The first, PrinAx, provides an orientation that uniformly distributes variability using the principal axes of inertia. The second, the Eye alignment, simulates a pupil location restriction condition that might occur with a helmet mounted display. The third, the TopHead alignment simulates a helmet position restriction that might occur when the helmet comes in contact with the top of the head.  

Figure 1. Ten subjects in each of three orientations. (Robinette 2007)

Each of these orientations makes different assumptions about how a helmet would be situated, and the human variability is not minimized with any of them. It is simply moved around. This is further illustrated in figure 2. In this figure we see all the head and face landmark locations for the same 10 subjects in each of the three orientations, from the front view. For PrinAx and Eye orientations the top of the head landmark has huge vertical variability, while for TopHead that variability is zero. For PrinAx and Top Head the pupil variability is huge but for Eye it is very small. The different orientations simply move the variability around based on theories and assumptions about fit location.

Figure 2. Landmark variability in different head orientations. (Robinette 2007)

Which one is correct? There is no way to know without putting the product on, but probably none of them. They are all just theories until verified with actual product fit.

Head Orientation Impacts Measurements

Some measurements use orientation in the definition, such as Tragion to Top of Head, or Pupil to Back of Head, or even Tragion to Glabella in the fore-aft direction. These measurements are unreliable and inaccurate if the orientation is wrong. The problem is illustrated in figure 3

Figure 3. Impact of orientation on z direction measurements. (Robinette et al 2025)

In this figure we see what happens to the Rhinion to Glabella distance in the z direction (fore-aft) when the head orientation changes. The subject doesn’t change, nor does the point to point distance (30 mm), but the z direction measurement fluctuates from 3 to 22 mm, a 19 mm range.
The standard deviations for head and face measurements that are not dependent on the orientation, such as Head Length and Face Breadth are approximately 8 mm for most populations. So, the error in the measurement caused by orientation error for just one subject, is as large as 2.375 population standard deviations for a typical head measurement. When the within-subject error, (like this), is larger than the between-subject error, (or standard deviation), that measurement is meaningless. The variability in the z direction measurement for each subject due to orientation error is larger than the full range of variability in the population’s point to point distance. This means that the z direction measurement is more a measure of orientation error than it is a measurement of a person.

If we are using direction based measurements to place or design a component the only orientation that will yield an accurate and meaningful measurement is one that uses the actual product placed on each subject. This yields the true orientation of the subject with respect to the product.

Product Prototype Orientation Allows for Understanding of Fit

Whitestone and Robinette (1997) illustrated how to create a product based alignment system for helmets. First three landmarks are places on the helmet to define an axis system, such as shown in figure 4. In this example, the three landmarks used to define the axis system were the Helmet Front Landmark, the Helmet Center- Left Landmark, and the Helmet Center-Right Landmark. The helmet was scanned by itself and the landmarks were identified and used to create the axis system. Then each subject was scanned in the landmarked helmet and their right and left pupil locations were identified. Figure 5 shows the pupil locations for a sample of subjects. This shows the true locations of the pupils in the helmet. This can be used to determine design changes needed for helmet mounted displays.

Figure 4. Establishing a product based axis system. (Robinette et al 2025)

Using prototypes of the product in a fit test, and scanning with and without the product can also help us understand product fit. In figure 6 we show two subjects in a top down view of one subject. (Product details are simplified for confidentiality reasons.) These two subjects have nearly the same measurements and comfort scores, but one failed for slippage while the other passed. Looking at the cross sections from the top view we see that subject 59’s Face Breadth was closer to the front of his head than subject 10’s face breath. This caused a gap at the back that made it looser on him. This is something we could only measure and view using 3D scans with and without the prototype product.

Figure 5. Pupil locations identified by having the subjects wear the product. (Bredenkamp 2025)

Figure 6. Two subjects in one size of headwear with their fit scores. (Bredenkamp 2025)

There are other ways to measure the interface of the product with the person as well. Figure 7 shows a heat map, or clearance/pressure map of the product with respect to the wearer. This is created by overlaying the scan or CAD file of the product on the scan of the subject and measuring the differences. It doesn’t tell us if the pressure is too much or not enough, but it does tell us where the product is on the person. Fit assessment scores can tell us if it is a good fit in the position or not. We recommend collecting fit scores, pass/fail results, anthropometry and scans in and out of the product.

Figure 7. Clearance/pressure map of product on person. (Bredenkamp 2025)

Summary

Virtual product placement without using a product prototype on a real person is unreliable. Fit assessment combined with 3D scans, with and without the product, provides us with the ability to visualize and understand fit in ways that were not possible before the availability of scanning technology. This is extremely useful during the product development process because it allows us to understand and make changes early in the process when changes are less expensive.

More information on how to use fit testing for head products in the design and development process is available in  Robinette, K.M., Veitch, D., Alemany, S., & Bredenkamp, K. (2025). Product Fit and Sizing: Sustainable Product Evaluation, Engineering, and Design (1st ed.). CRC Press. https://www.routledge.com/Product-Fit-and-Sizing-Sustainable-Product-Evaluation-Engineering-and-Design/Robinette-Veitch-Alemany-Bredenkamp/p/book/9781032491189

If you would like someone to help, Anthrotech provides this type of consulting service.

References

Bredenkamp, K. (2025)  Head and Face Wearables, in Product Fit and Sizing: Sustainable Product Evaluation, Engineering, and Design, chapter 6, pp. 268-338, CRC Press, ISBN 9781032491189

Robinette, K. M., Veitch, D., Alemany, S., and Bredenkamp, K. (2025) Product Fit and Sizing: Sustainable Product Evaluation, Engineering, and Design, CRC Press, ISBN 9781032491189

Robinette, K.M. (2007) Maximizing Anthropometric Accommodation and Protection, AFRL-RH-WP-TR-2008-0022, August 2007, Air Force Research Laboratory, Human Effectiveness Directorate, Biosciences and Protection Division, Biomechanics Branch, Wright-Patterson AFB OH.

Whitestone, J.J. and Robinette, K.M. (1997) Fitting to maximize performance of HMD systems, in Head Mounted Displays, Designing for the User, editors Melzer, J. and Moffitt, K., chapter 7,pp. 175-206, McGraw Hill Publishing, New York, New York.  

• The average “person” doesn’t exist
• A model with all average dimensions isn’t shaped like a real person
• People with 5th and 95th percentile values for more than 4 measurements are highly unlikely
• 5th and 95th percentile people are impossible to construct

If ever you have used these methods, they didn’t work and if you were wondering why read on…..

Mythical Average People
In 1952 Gilbert Daniels tried to find one man out of 4,063 men who was average for everyone of 131 measurements. He defined the average as middle 30% for each variable. After just 10 variables there was no one left, who was average for all 10. Everyone was smaller or larger than average for something. An illustration of this phenomenon is shown in figure 1. It shows the percentile values for 3 subjects, for 10 measurements. Every subject is in the middle for some measurements and either larger or smaller than the middle for others. Subject 89 is both below and above the middle for some measurements.

Figure 1. Proportions for three random subjects (from Robinette et al 2025)

While Mr. Daniels demonstrated that there is no average man, some people still believed that designing for averages was a good idea. However, even this proves to be incorrect. In 2004, the HFES 300 Committee illustrated that if an average model is constructed, it has a different shape than any of the subjects in the sample. This is illustrated in figure 2. It shows 3 “subjects” with small, medium, and large proportions and we show their average. The average shape is different and too small for all three. Therefore, if we use a statistical average construct we may not fit anyone.

Figure 2. Average may not fit anyone.

This means a product designed for the average, 2x2x2, will be both too small and at the same time too large for each individual, 3x2x1, as shown in figure 2. The new product may not fit anyone. Some people might find this surprising because some schools still teach this method, but the unintended consequence of design using an average shape and then not fitting customers has been well known now for more than 70 years. When designing using the average didn’t work, design teams tried the percentile approach.

5th to 95th Percentile Myths
Now some people believe that the answer is to design around the 5th and 95th percentiles, with the belief that this will accommodate 90% of the population. This is also not true. In fact, it is worse because the 5th and 95th percentiles do not add up.

Figure 3 is taken from the SAE technical paper by Robinette and McConville (1982) and illustrates that when 5th or 95th percentiles are calculated for segments of a measurement (in this case stature), they do not add up to the 5th or 9th percentile of the measurement. In this example, 7 segments were calculated for each person in a sample of 3,235 women by subtraction such that the sum of the 7 segments was equal to the Stature. Then the 95th percentiles were taken for each of the measurements and added together the sum was 188.81 cm. However, the 95th percentile Stature was only 173.06 cm. The sum of the 95th percentiles of the parts was 15.75 cm larger than the 95th percentile of the sum. To re-state this, while Stature was always equal to the sum of the 7 segments in the sample, the sum of the 95th percentiles for the 7 segments was larger than the 95th percentile Stature. This results because the person who is 95th percentile in size for one measurement is not the same person who is 95th percentile for another. So, when we combine percentiles we are combining body parts from different people.

Figure 3. Sum of percentiles of the parts does not equal the percentile of the sum.

The other issue with 5th or 95th percentile values is the probability of being 5th or 95th for more than one measurement of dimension can be so small as to be impossible. For example, the probability of a person being 95th percentile or greater when the measurements are not correlated is shown in table 1.

Table 1 Probability of being greater than 95th percentile for uncorrelated variables.

The probability of being larger than the 95^th percentile for every one of 4 variables is less than one person out of 100,000. How many until we can agree that it is impossible?

If using averages and percentiles doesn’t work for design. What does?

The Case for Cases

The best representations to use are individual people rather than statistical constructs. We call these individuals “cases”. A case can have three forms: 1) a list of measurements of an individual, 2) a 3-D or 4-D model of an individual, or 3) the actual individual. The actual individual is sometimes called a fit model or a live model. When we use an actual person, we know we will fit at least one person and probably lots of people who are like that person. If we choose well, we can maximize the number of people we accommodate in the fewest number of sizes.

More information on how to select good  cases can be found in Chapter 3 of Robinette, K.M., Veitch, D., Alemany, S., & Bredenkamp, K. (2025). Product Fit and Sizing: Sustainable Product Evaluation, Engineering, and Design (1st ed.). CRC Press. This chapter can be downloaded for free here:https://doi.org/10.1201/9781003397533

However, if you would like someone to help, reach out to Anthrotech which provides this type of consulting service.

References

Dainoff, M., Gordon, C., Robinette, K.M., & Strauss, M. (2003). Guidelines for using anthropometric data in product design, HFES Best Practices Series 2003, Human Factors and Ergonomics Society.

Daniels, G. S. (1952). The average man? AIR FORCE AEROSPACE MEDICAL RESEARCH LAB WRIGHT-PATTERSON AFB OH. https://apps.dtic.mil/sti/tr/pdf/AD0010203.pdf

Robinette, K. M., Veitch, D., Alemany, S., and Bredenkamp, K. (2025) Product Fit and Sizing: Sustainable Product Evaluation, Engineering, and Design, CRC Press, ISBN 9781032491189

Robinette, K.M. and McConville, J.T. (1982).  An Alternative to Percentile Models.  SAE Technical Paper 810217, in 1981 SAE Transactions, pp. 938-946, Society of Automotive Engineers, Warrendale PA.

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While DHMs are great for getting us close to the desired design, they aren’t perfect, especially when it comes to accounting for the human experience. Here are a few real-life examples where human feedback revealed flaws that DHMs missed:

1. “I know the computer model wears it that way, but I can’t. It won’t go there.”
   Just because it works for a digital model doesn’t mean it will work for a real person.
   
2. “This either interferes with my utility belt or chokes me, or both, depending on where it is adjusted.”
   DHMs often miss situational complexities like how something interacts with other gear or accessories.
   
3. “It feels like I’m in a tunnel, and it makes me uncomfortable or claustrophobic.”
   DHMs cannot replicate the psychological experiences or discomfort that people might feel when using a product.
   
4. “Something is jabbing me in the back of the head.”
   An unnoticed adjustment clip caused discomfort for real users, something a DHM would never mention.
   
5. “I can’t wear these. It’s fine standing up, but hurts when I sit or squat.”
   Fit and comfort vary depending on body position, a factor DHMs often fail to capture.
   
6. “Well, the DHM may say I should see something, but I don’t.”
   Vision, perception, and body dynamics in real life can differ greatly from a computer simulation.
   
7. “I feel stupid wearing this. You couldn’t pay me enough to wear this in public.”
   Human emotions and social factors—like how people feel wearing a product—are completely overlooked by DHMs.
   
8. “It feels like it will fall off if I move too fast, and it makes me nervous.”
   Stability and confidence in wearing a product are critical, but DHMs won’t express anxiety or discomfort.
   
9. “Yes, it slips a lot if I move too fast, but I like how it feels. The recommended size hurts.”
   Real feedback about sizing preferences can vary widely, something that CAD systems rarely accommodate.
   
10. “It’s uncomfortable, but I’d still buy it because it makes me look good.”
    Style can outweigh comfort for some, a factor DHMs are unable to weigh in on.

The takeaway? CAD systems and DHMs are great starting points, but they lack the ability to consider the psychological, emotional, and situational experiences of real users. Relying on these tools alone can lead to missed insights, costly design revisions, or even product failure.

While technology continues to improve, there’s currently no digital solution capable of replicating the full human experience—especially when it comes to preferences, discomfort, or even fashion choices. A quick round of real-world testing can help avoid significant setbacks and ensure the product you deliver truly meets the needs of your customers.

In some cases, as with examples 9 and 10, human feedback can even help you refine product sizes or avoid unnecessary variations, ultimately saving money and resources.

Bottom line: DHMs are valuable, but never underestimate the importance of human feedback in product development. It’s the missing piece that bridges the gap between good design and great user experience.

Interested for more in-depth information on this topic? Click here and send us a message requesting the latest on Cases and Fit Models.

Anthrotech has long been synonymous with expertise in anthropometry, the science of human dimensions. While our usual client projects involve supporting companies in understanding human body proportions for product design and usability, Anthrotech recently made headlines for its work with the Federal Bureau of Investigation (FBI) for a high-profile criminal trial.

In 2022, the FBI contacted Anthrotech for their assistance in a complex case involving the suspicious death of Bianca Rudolph, the wife of Pennsylvania dentist Lawrence Rudolph, during a hunting trip in Zambia’s Kafue National Park. Lawrence Rudolph claimed his wife’s death was accidental, but the FBI harbored doubts, especially considering substantial life insurance payouts.

With little physical evidence and conflicting narratives, the FBI turned to Anthrotech for expert testimony. Led by Dr. Bruce Bradtmiller, former owner and now Senior Consulting Scientist Emeritus, Anthrotech conducted a meticulous study to measure several anthropometric dimensions that would support or refute various claims and hypothesis.

The study, which involved women of similar dimensions to Bianca Rudolph, debunked the likelihood of accidental firearm discharge. Thirty-six participants meticulously replicated the scenario, revealing critical insights that bolstered the FBI’s case against Lawrence Rudolph.

Anthrotech’s collaboration with the FBI didn’t just stop at providing data; it extended to national television. The company was featured in an episode of NBC’s “Dateline," showcasing their groundbreaking work and underscoring the significance of anthropometric analysis in criminal investigations.

For Anthrotech, this wasn’t about determining guilt or innocence; it was about delivering objective data on human body dynamics. As society evolves and interactions with products become more intricate, Anthrotech envisions expanding its focus to encompass studies in occupational ergonomics and human-device interactions.

Oscar Meyer, Anthrotech’s President and CEO, emphasizes the company’s enduring relevance in litigation, highlighting the ubiquitous nature of human-error scenarios. Whether it’s in civil cases or criminal trials, Anthrotech remains committed to providing invaluable insights into human dimensions and behaviors.

Check out the article at https://ysnews.com/news/2023/01/anthrotech-takes-expertise-to-courtroom which recently won the “Best Local Feature” reporting award presented by the Ohio News Media Association.

Fit Matters: Don’t Impede the Function

When it comes to creating products for human use, be it a virtual reality headset, a seatbelt, or even the interior of a truck cab, getting the fit right is essential. Believe me, you wouldn’t want a virtual reality headset that pinches your head or a car seat that makes you feel like a sardine.  Creating incredible brand experiences demands that users’ interactions with products deliver a comfortable fit and enable the highest levels of user performance. 

But here’s the catch – it’s not always as straightforward as it seems. Sometimes, identifying the right body part to measure can be a real head-scratcher.

The Not-So-Obvious Measurement

Misidentifying what needs to be measured can lead to products that simply don’t work as they should. A personal experience of one of the Anthrotech teammates highlights this issue. During a holiday visit with his grandkids in Minnesota, they were deciding who should sit in the backseat of the car between two car seats. He ended up in the back, as he had the smallest “derriere." Well, it turns out that those car seats were wider at the top than their base, and his shoulders suffered the consequences. Quick measurement guess equals an uncomfortable car ride!

The lesson here? Measuring body parts for product design can be tricky business, and making assumptions can lead to ill-fitting products on store shelves.

More to Consider than Which Body Part

Let’s not forget that it’s not just about which body part to measure but also considering body capabilities, like reach and effective reach. Take the example of designing an assembly line with belt, bin attachments, and controls. You may measure arm reach to determine where to place bins and controls, but it’s not that simple.

Heavier employees may have different effective arm reaches due to their protruding stomachs, which must be considered for the product to work well for all users. In product design, it’s not just what you measure but how you interpret and apply those measurements.

Recreating Measurement Offers Additional Challenge

So, you’ve got the right body part to measure, and you’ve considered body capabilities. What’s next? Well, once you have good data from anthropometry experts, it’s time to develop a prototype and test it on real, living test subjects. Measuring critical dimensions on these subjects ensures that the product fits a representative range of people.

However, here’s the catch – you must use the same techniques to measure test subjects as were used when collecting the original data. Any discrepancies in measurement techniques can lead to inaccurate results, and that’s a big no-no in the world of product development.

Count on the Experts

Designing products for diverse populations is a challenge, and there’s a lot of room for potential error. However, the good news is that anthropometry experts have faced these challenges head-on. We know exactly what we’re looking for, can help developers pinpoint the right body part, and ensure that all measurements are uniform and accurate. So, rest assured, there are experts out there to guide you in this fascinating world of measuring the right body parts for product design.

In the end, the key takeaway is this: when it comes to creating products that fit just right, it’s not just about the size of your body parts – it’s about how those parts will be moving when the user is engaging with the product. So, the next time you put on a well-fitting seatbelt or enjoy a comfortable virtual reality experience, you’ll know that it’s all thanks to precise measurements and experts who’ve got it down to a science.

Whether you’re creating products for men, women, or both, it’s time to address some common misconceptions surrounding the body sizes of males versus females. So, grab your thinking cap, because in today’s blog, we’re going to debunk some of these myths and set the record straight.

Myth #1: Women have bigger hips than men.

The Truth: Yes, in absolute terms, women do have wider hips than men. But it’s not just about width. What’s crucial to remember is that female hips are wider in proportion to the rest of the body.  So, it’s not just about size; it’s about the balance and the overall shape.

Myth #2: Women have larger chests than men.

The Truth: It’s time to bust this one too. Women do indeed have more breast tissue than men, but when it comes to the chest as a whole, men actually take the cake. Men boast larger chests in terms of depth (distance from front to back) and circumference (the distance around). The secret here lies in the male rib cage, which is not only broader but also deeper than a female’s rib cage. So, it’s not all about the bust!

Myth #3: Men have bigger hands than women.

The Truth: It’s true that men’s hands are larger in both length and width compared to women’s. But hold on – there’s a twist. Female hands are proportionately longer in terms of the ratio of length to width. Any glove designers out there might find this tidbit intriguing.

Now, here’s the kicker: there are many instances, especially in fields like law enforcement or the military, where a product initially designed for men needs to be used by women for the first time. While the temptation might be to take an educated guess when adapting a male-oriented product for a female user, the competitive market landscape of today demands more. As user experience is becoming the differentiator to give companies their competitive edge, more and more product designers are diving deep into data to create products that fit better. Whether we’re talking about body armor, protective gloves, respirators, or police uniforms, accurate input data leads to designs that are not only more comfortable for the customer but also safer and enable users to perform more effectively.

Both male and female users benefit from designs that consider the critical differences in body size and shape between the two sexes. And the beauty of it is that better-fitting products not only delivers a better user experience but also helps capture more significant market share and generate a higher return on investment for new product innovation.

In conclusion, it’s high time we put these myths to rest. When it comes to product design, it’s all about embracing the facts and tailoring products to meet the diverse needs of both men and women. After all, we’re all unique individuals, and products should reflect that diversity.

1. Preparatory Planning: Lay the Foundation for Success

The foundation of a successful fit test lies in your preparatory planning. This stage involves several critical tasks, such as:

• Developing the fit criteria in collaboration with the product designer
• Crafting a test protocol that outlines the step-by-step process for the test
• Testing the protocol on practice participants to identify and resolve any issues
• Setting up electronic data collection for the anthropometric fit test data, among other logistical necessities

If you’re testing garments, it’s essential to measure them before testing begins. This step ensures that the garments are accurately sewn, within seam tolerance, and perform precisely as the designer intended. By addressing any issues with the measurements or functionality beforehand, you’ll be better prepared for the fit test.

2. Well-Coordinated Logistics: Keep the Wheels Turning Smoothly

Fit tests can involve a cast of dozens – design team members, test participants, the measurement team, and the data collectors. With so many moving parts, well-coordinated logistics are the backbone of your operation. Having a dedicated coordinator collaborating and communicating among everyone involved can be a game-changer. They help ensure that the right people show up at the right time and in the right order.

The location of the fit test also plays a significant role in the logistics. We’ve seen it all, from dark and cramped stairwells to broom closets and even aboard ships. But the golden rule is this: well-lit, temperature-controlled, open spaces are the best. Consider doing a site visit before the fit test day to iron out any logistical wrinkles that could affect data collection.

3. Measure the Right Body Parts: Get the Essentials

Measuring the correct anthropometric dimensions associated with your garment or product is absolutely crucial. Depending on the product, you might need to measure the same dimensions on the body in different positions, such as standing and sitting. For example, when it comes to body armor with stiff ceramic plates, it’s vital to assess the fit while standing to ensure adequate torso coverage. But assessing it while seated verifies that the plate doesn’t interfere with the thighs or the neck. So, in such cases, measuring both standing and seated dimensions, like chest breadth, waist front length, and waist back length, becomes essential.

4. Measure the Right People (and Enough of Them): Represent Your User Base

Your fit test participants should represent the intended users of the product. After all, testing firefighter turnout coats on high school students doesn’t quite cut it! To obtain confidence in the results, you’ll need to test enough people, especially for sized items such as clothing or body armor. Ideally, participants should cover each intended size, and recruiting individuals who fit each size is essential. For unsized items, be sure to include participants with a wide range of anthropometric dimensions, ensuring the product works well for all intended users. Fit tests are a necessary and rewarding part of product and apparel design. We hope these tips, combined with the right partner to guide your strategy and execute your test, will lead to a successful fit test that results in products that outperform their sales forecasts, without a deluge of returns. After all, who doesn’t want a product that fits like a glove and works like a charm?

Fit tests are a bit different from your typical anthropometric surveys. While surveys focus on collecting data about people, fit tests are all about evaluating how well a product interfaces with the person being measured. When done correctly, they can help verify sizing and identify if certain sizes are even necessary. This can save companies a significant amount of money in the long run.

Now, let’s get into the nitty-gritty of determining fit test sample sizes.

First off, most anthropometric dimensions follow a normal distribution, like a bell curve. You’ll find a lot of people in the middle and fewer at the extremes. However, products with various sizes need to fit not just the average Joe but also those on the smaller and larger ends of the spectrum. So, if we recruited people for a fit test based solely on the anthropometric distribution, you might end up with only one person needing an XX Small or an XX Large size. Testing just one person for a particular size isn’t very reliable. To get a more accurate representation, you’d need proportionately more people at the extremes. This means your fit test should include a variety of sizes, and this translates to testing more people to ensure each size is adequately represented.

Another factor to consider is ethnic representation. In general, for anthropometric surveys, it’s important to have data from various ethnic groups. However, when it comes to fit tests, the distribution of sizes matters more than ethnic or age representation. It doesn’t mean you should only sample one ethnic group, but the focus should be on getting the size distribution right.

So, what’s the magic number for the right fit test sample size? Well, there’s no one-size-fits-all solution here. At Anthrotech, we’ve conducted fit tests with as few as 50 participants and as many as 2,200, with most fit tests falling somewhere in between. The ideal sample size depends on the specifics of your project, and that’s where our experts can collaborate with product development teams to help. We can discuss the size and scope of your project to make sure you get the results you need while saving time and money in the process.

The Case for Scanned Dimensions: Unleashing 3D Magic

First up, let’s talk about the magic of 3D scanning. Those incredible whole body, head, hand, and foot scanners open up a world of possibilities. Why? Because they allow us to collect a massive number of data points. It’s like a data bonanza! Scanning technology offers two big advantages:

Efficiency: With scanning, you can rapidly collect human measurement data. It’s not just quick; it’s turbo-charged. Plus, it unlocks previously unavailable shape data.

Critical Data Points: Some measurements are hard to get with a measuring tape. Take pants, for example. You’d want to know both the hip circumference and waist circumference, right? With a tape, you measure them separately. But with a scan, you nab both of these measurements simultaneously, along with the distance between them and the relationship of the hip and waist in the front and back. Scans are the superheroes of capturing the shape and contour of the body, which can be nearly impossible to achieve with a tape.

Imagine the product development challenge of designing a respirator mask.  A mask that needs to snugly fit around the contours of the nose and mouth to safely protect the user. Scans reveal the precise shape, enabling mask designers to create a mask that fits like a glove, or rather, like a well-fitted mask!

The Case for Measured Dimensions: Back to Basics

Now, let’s talk about the classic measuring tape. There are situations where this old-school method still reigns supreme. Take clothing, for instance – it’s traditionally measured with a tape. And if you’re sizing up a hat or helmet, you want to account for dimensions on the head, including areas where hair might be compressed. You can manually compress the hair when measuring with a tape, but a scan captures the head and hair in their natural, uncompressed state.

When a person is in everyday clothing, measuring with a tape is usually a breeze because you can get close to the body under the clothing. Scanning, on the other hand, includes the surface of the clothing but might miss the actual body beneath, depending on the type of clothing.

Additional Considerations: Time, Budget, and Error

Now, here’s the twist – it’s not always about saving time. Scanning doesn’t instantly produce measurements; they need to be extracted from the scan. If it’s an automatic process, it might be faster but less reliable. For high-accuracy measurements, a skilled technician places landmarks on the scan, which can be time-consuming, depending on the number of landmarks.

Budget might not be a deal-breaker either. The major expense in large surveys is often setting up the team and recruiting participants, and there isn’t a significant added cost for combining scans and traditional measurements.

When it comes to minimizing errors, training and practice are key, regardless of the method used. Whether you’re taking traditional measurements or placing landmarks on scans, the technician should be well-trained and experienced. 

In the grand scheme of things, both methods have their merits. That’s why most projects opt for a blend of traditional anthropometric measurements and 3D scanning. At Anthrotech, we work with product development teams to weigh the benefits of both approaches and craft a customized solution that aligns perfectly with their unique goals. After all, it’s all about striking the right balance!