The mechanical principles of human motion have long been the subject of scientific research. For instance, in his book De motu animalium [3], Giovanni Alfonso Borelli, who lived in Italy from 1608 to 1678, described mechanically as systems of lever arms, deflection pulleys and ropes, the interplay between muscles and bones to generate the motions that can be observed in animals and nature. Since then, much research has been done in the field of biomechanics as it became known. With the development of computer simulation techniques, biomechanical models of humans have been developed to better analyze human motion.
For MBS simulations, the human body can be modelled as a marionette consisting of 17 rigid bodies. For the MBS model, it is necessary to provide the mass and inertia tensor for each of these 17 segments. Along with these, some geometric properties have to be specified to define the location of the joints. There are two principal ways to parametrize these body properties for each segment: determine the parameters like the mass, inertia and geometric properties for one specific subject for individual computer-aided simulation, such as for surgical planning or to analyze an athlete’s performance; or, which is more common, build human body models on statistical bases using some established data sets (see examples in the references [4,5,6]).
To better understand this statistical concept, Da Vinci’s famous image of the Vitruvian man very nicely illustrates how the human body features some basic symmetries or proportions that make it possible to deduce the different segment lengths based on the subject’s height. The use of statistical data and regression equations represents one approach to modeling the human body. Another is to calculate the mass properties of the segments based on the calculated volumes (cylinders, truncated cones and ellipsoids) and the mean densities. One such prominent HBM is the Hanavan model [7], but this method requires many segment lengths and radii to be measured and therefore more effort during the modeling process.
Since these two main methods to determine the parameters differ – one results in a more individual model but requires higher effort, and the other is quite easy but describes a “mean” body, we decided to use the method which is easier to deploy in engineering contexts for the model wizard. Consequently, the VariBody model wizard for RecurDyn builds a complete HBM using just three input parameters: body weight, body height and gender to calculate the segment parameters by means of regression equations.