Last week, everybody who is somebody in biomechanics gathered for the 7th World Congress of Biomechanics in the great city of Boston, Massachusetts. Several national and continental organizations serve the biomechanical community, and they all have their own congresses, but every four years all of them get together in one big and all-encompassing conference. This time, the World Congress hosted more than 5000 delegates.
For me personally, and for several of my closest colleagues, this was a very positive event. There is a much-increased interest in simulation methods in general and in the AnyBody Modeling System in particular. It has been a long time coming, and I have felt at times that we had to explain over and over why it is reasonable to believe that the laws of mechanics apply to the human body and that we would, despite challenges, get it right if we stuck to that belief and kept refining the models and software.
This year, many applications of AnyBody were presented by independent research groups, some of them by unrelated initiatives and some under a competition named “The Grand Challenge”. A visionary group of scientists headed by B.J. Fregly, Darryl D’Lima and Thor Besier have now five times published data sets containing in-vivo measurements of knee forces from an instrumented implant and challenged the simulation community to predict these forces. Every generation of data contains a new activity, and the contestants initially do not know what the correct forces are, so it is a truly blinded test. After the estimates have been submitted, the true values are revealed, and the participants are now challenged to improve their models and predictions.
For this year’s competition, my colleague Michael Skipper Andersen headed a strong group of scientists related to the TLEMSafe project, more precisely Marco Marra, Valentine Vanheule, René Fluit, Nico Verdonschot and yours truly. Not only did we win; our prediction was the closest in the history of the competition. The second place went to a Korean group also using AnyBody but with a completely different model. This should finally silence any doubt that musculoskeletal simulation indeed can simulate forces inside the body.
I often compare the evolution of musculoskeletal modeling with the development and adoption of finite element analysis. When I was a student in the 1980’ies, I was extremely fascinated by the possibilities of finite element analysis for the solution of engineering problems when, and I did my PhD in this field, developing my own shape optimization system and its associated finite element solver bottom-up in Pascal. It was quite challenging at times, due to the lack of programming tools and debuggers but also because of the lack of understanding. Many older professors completely misunderstood the project, even when the results began to appear, and I remember particularly one instant when a slightly cranky one cornered me and started shouting agitatedly into my face that “this finite element shit will never amount to anything – ever!”
Time proved him wrong and very few of the hi-tech products we use today, from mobile phones to wind turbines, could have been developed in the absence of finite element simulation in the design process. I have felt since the beginning of the AnyBody project that musculoskeletal modeling is on the same path and holds the same potential.
To accomplish that goal we must also look to the way CAE in general is used: We rarely make finite element models of bridges that are already built or last year’s car model. We make models of future bridges or the bodies of cars to be marketed in three or five years; we simulate the future. The real challenge of musculoskeletal modeling is to make the technology reliable enough to be used for prospective analysis. So far, most musculoskeletal models have simulated retrospective situations for which experimental input data, for instance from motion capture and force platforms, is available, i.e. the past. This might be interesting for research but it is not what makes the technology valuable to a large group of users in healthcare and in the industry. The real potential is simulation of situations that may happen in the future: the outcome of a possible surgical procedure, the behavior of a new type of joint prosthesis, the ergonomic quality of a new hand tool or working environment being designed.
To make this happen, we must meet at least three additional challenges:
- Models must be able to represent individuals for healthcare applications as well as statistical cross sections of the population for product design.
- Models must be independent of force input, typically from force platforms.
- Models must be independent of motion input.
These three challenges will define much of the research of my group in the forthcoming years. Let me try to give a brief status on this:
Statistical shape modeling was a big topic at the WCB2014 and in the biomechanical community in general, and this will eventually benefit AnyBody models. We have also with good partners made much progress in the realm of the TLEMSafe and A-FOOTPRINT EU projects in terms of individualization of the models. We can do it, but it takes time and the workflow must be improved.
AnyBody relies on inverse dynamics, which has a legacy from classical motion analysis. It is therefore a popular misconception that force input is absolutely needed. This has never been the case due to the very general mechanical formulations used inside AnyBody, but we have used force platform input when it was available because we thought that it is better to use real data when we have them. At WCB2014, Michael Skipper Andersen with co-authors Fluit, Kolk, Verdonschot, Koopman and Rasmussen presented the paper “Prediction of Ground Reaction Forces in Inverse Dynamic Simulations”, which very convincingly shows that ground reaction forces can be predicted with great accuracy from kinematics and without increased computation time.
The final, and severest, challenge is to predict motions. Saeed Davoudabadi Farahani from the AnyBody Research Group recently had a paper accepted that convincingly predicts cycling motions, and another paper on squat jump motion prediction is under review. Stay tuned for those. The status in this field is that we can predict simple motions reliably, but the computation times are still too high and there are open questions regarding prediction of abstract working tasks.
We will not run out of scientific challenges any time soon, but we are up to them and WCB2014 shows that we have come a very long way.
I wish everybody on the Northern Hemisphere a great summer vacation.