A recent research article summarizing a study designed to update the anthropometry data for US based truck drivers reveals some interesting statistics about truck driver size and safety, and also brings to light the fact that ergonomists and designers often don't have accurate data, even if such data is critical to the design outcome. In this article I'll review the study and also share some thoughts on the importance of up-to-date anthropometry data, as well as new developments in the way it's used to create more accommodating designs.
Researchers Jinhua Guan, Hongwei Hsiao, Bruce Bradtmiller, Tsui-Ying Kau, Matthew R. Reed, Steven K. Jahns, Josef Loczi, H. Lenora Hardee and Dominic Paul T. Piamonte teamed up to conduct a large-scale anthropometry study of US truck drivers in order to update the body size data and discuss its implications on cab design models. Previous to this study, designers were relying on outdated and inaccurate data that was 25 to 30 years old.
In their introduction, they review these telling statistics:
Guan et al note that designs that better reflect actual driver population anthropometry can improve working conditions for the drivers, but they will also affect driver safety and that of other road users. As they put it,
If the design of the truck cab is poorly fitted to the size and dimensions of the driver, the road may be less visible, driving controls may be more difficult to reach, and seat belts may be less comfortable and less likely to be used—all of which increase the risk of injury to the driver and other road users.
The authors also discuss a shift from using a traditional percentile based design approach to a more encompassing multivariate accommodation model (MAM). As they describe it,
The 5th-to-95th-percentile approach has been criticized for the decrease in accommodation when two or more dimensions are involved in a design … and for its inability to generate biofidelitic models … The MAM approach offers a superior solution to the workstation design because of its ability to circumvent both problems.
[PB: I had to look up biofidelitic. dictionary.com does not recognize it, and google came up with only two instances of the word; one in the research article I'm reviewing herein, and another in an obscure military contract. However, I think we're safe to assume that it has do with the ability of a model of a biological system to accurately reflect the biological system being modeled.]
I'll discuss more about percentile vs. MAM approaches in "What this Might Mean to Ergonomists," below.
Interested readers are encouraged to read the original article, referenced below, for complete details on study methods and results. However, briefly, key methods include:
A series of complex statistical methods were then applied to the data to develop anthropometric models useful in cab design.
Results of particular interest include:
Comparing current USA truck drivers with the USA general population …
Comparing current USA truck drivers with truck drivers of 25 to 30 years ago (males only, because there was not enough female data in the older studies), the researchers found that they were not statistically different in height, but are statistically different in other dimensions:
Though describing the MAM approach is beyond the scope of this article, in a nutshell, the approach was used to create 15 body models each for males and females. The approach involves statistically testing combinations of multiple body segments as principle components (PCs) of the models. For males, three PCs were found to account for 88% of the variability in the data:
The same PCs emerged for the female models, accounting for 53%, 21%, and 13% of the total variation, respectively.
What This Might Mean for Ergonomists
Anthropometry is a cornerstone of physical ergonomics. It's one of the most important kinds of data we draw on when designing equipment and workspaces, yet accurate, up-to-date data can be hard to come by. As this study illustrates, human anthropometry changes over time and across populations of interest.
For example, these researchers found that the USA truck driver population is significantly heavier than the general USA working age population, and their body width and circumference measurements are also larger. Imagine the implications on truck cab design if designers rely on general population data rather than data specific to the demographics of the population of actual users, real truck drivers, in this case.
In general, designers who do not understand or appreciate ergonomics often make this type of error, at great consequence to the user experience, performance and safety of the user population. And in the case of trucking, and many other types of equipment design, the safety implications spill over to affect other system users, other drivers and transportation infrastructure in this case.
But this study also demonstrates that the age of our data sets can also significantly impact design. In the case of this truck driver population, 25-30 years resulted in significant increases in weight, seated abdominal depth; forearm-forearm breadth; seated hip breadth; and waist circumference.
During that same time period, however, the average height of the male truck driver population decreased. While this may seem counterintuitive to those who do not understand ergonomics, it actually makes perfect sense when we consider how the demographics of the truck driving population have changed since the 1970's and 1980's: more Hispanic drivers have entered the workforce, and the male Hispanic population is, on average, shorter in stature than other ethnic/racial demographics.
It is fortunate that the truck design industry teamed with NIOSH and others to develop this data set, because this industry now has up-to-date, presumably accurate data to work with. However, not every design population is so well understood and measured, and it's a never-ending struggle to keep anthropometry data sets usable and up-to-date.
Another key point that surfaces in this research article is the MAM approach vs. the percentile approach when applying anthropometry data in a design. We can often rely on simple percentile-based functional anthropometry data in workplace ergonomics. For example, overhead reach height is a single dimension that could be of interest, or seated eye height, or forward reach distance. We are safe using percentile-based data in these cases to fit, for example, the 95th percentile male. However, for designs that require more than one gross anthropometric measure, like a truck cab that has 3-dimensional spatial concerns affected by body postures and various other factors, the multivariate MAM approach should be applied in order to accommodate the greatest number of users. The Ergonomics Report™ will address this topic in greater depth in a future article.
This study drives home the need for a human-centered design model — the model ergonomists champion. For any design, whether it be as simple as a hammer or as complicated as a truck cab, start by carefully defining the intended use and desired user task outcomes and the target user population. (For added measure, also consider the unintended foreseeable uses.). Next, place that user population in the design environment, performing all of the intended tasks, and design the system around that population to elicit safe and effective human performance.
The alternative, with which the world is all too familiar, is to design the system according to technological constraints first, then at some point, often when it's far too late to influence the design outcome, consider the user as an afterthought. When designers don't take the time or interest to deploy a human-centered design strategy, users end up having to adapt to poor designs. Predictably, the user experience suffers, and in the worst cases result in catastrophic accidents like the Three Mile Island nuclear facility disaster, the Bhopal, India chemical leak disaster, a car accident that killed or injured a family member, or any number of accidents that have their roots in poor ergonomics.
Jinhua Guan, Hongwei Hsiao, Bruce Bradtmiller, Tsui-Ying Kau, Matthew R. Reed, Steven K. Jahns, Josef Loczi, H. Lenora Hardee and Dominic Paul T. Piamonte, U.S. Truck Driver Anthropometric Study and Multivariate Anthropometric Models for Cab Designs, Human Factors, October 2012; 54 (5), doi:10.1177/0018720812442685. At the time of this writing this article was available to Human Factors subscribers at: http://hfs.sagepub.com/content/54/5/849.full
This article originally appeared in The Ergonomics Report™ on 2012-10-17.