Most human factors guides contain some information on anthropometric data and its application. Some of it is very general, but practitioners should be aware that it exists. Some useful links:
Some useful anthropometric data is available on the web. I've compiled some of it on my downloads page. Other sources include:
Ergonomics and applied anthropometry are performed using software representations of human figures. These digital human models (DHM) can represent people with a wide range of body sizes and capabilities. I conduct research that aims to improve the capabilities of these models. The links below connect the publisher's websites. I have extensive experience with Jack; a small amount of (mostly out of date) experience with RAMSIS; and essentially no firsthand experience with the others.
The above are what I consider to be the "high-end" commercial systems. These have the most features and the best integration with commercial CAD packages. Quite a few other human modeling systems are in use, and a subset of those are commercially available. Some of these are:
We use a variety of commercial tools for our research. We've chosen these systems based on price and performance as well as compatability with our partners and sponsors. In all cases, we have developed software tools and new procedures to make maximum use of the capabilities of the hardware.
It's been said that you begin to understand something when you can create a model of it. And when you can write software to simulate it, you're getting somewhere. So I'm plugging some software tools that make a huge difference in my productivity by allowing me to rapidly analyze data and create simulations. I'm not saying these are the best tools for any particular project. But having used a lot of programming languages and environments, these are the ones I stick with.
Most people who know Mathematica exists are aware that it can solve symbolic equations. But that misses the real point, which is that Mathematica is a very elegantly designed and hugely powerful software system. It solves symbolic equations, and differentiates, and integrates, and all that great math stuff, but it's also a very flexible programming environment that includes powerful functional and pattern-based programming techniques. I use Mathematica on nearly every project. It's my main data processing tool, my main graphics programming tool, and a prototyping tool for most algorithms I develop.
Every programmer has a favorite 'scripting' language. My choice is Python. I was introduced to Python because the JackScript interface to the Jack human modeling software is written in Python. Python is something like the opposite of Perl, because it has a clean, human-readable syntax. Python's not a language for writing a killer one-liner. It's a language for writing easily understood, reusable code. Python can be used interactively, which is great for debugging, but is most powerful as programmming language. It's fully object-oriented, supports some powerful functional programming constructs, and it's free.
Having gone through many statistics software packages over the years, I'm glad to have been introduced to R by a statistician colleague. R is an open-source version of the S language and has, roughly speaking, the same relationship to academic statistics as SAS has to commercial statistics. Yes, it's yet another programming language to learn, but it's worth the effort just for the very high quality plots. Moreover, many cutting-edge statistical algorithms, or worthwhile innovations on classical tools, are available in a bewildering array of downloadable, free, reasonably well documented code. Can you trust R? Many, many academic statisticians, and not a few in industry, do everyday.
Just think if you took all that time that you spend maintaining your Windows system and spent it productively? All of the power of UNIX at your fingertips and approximately zero time spent worrying about malware. And if you absolutely, positively have to use Windows (and I do, sometimes), it's still time to get a Mac -- running Windows in a virtual machine.
©2017 Matthew P. Reed and The University of Michigan