Natal – coming together

Biomechanics like every scientific field has its main conferences. The International Society of Biomechanics hosts a biannual congress that attracts around 1000 researchers from literally all over the world, and these conferences are among my absolute favorite events.

This July the event was in Natal, Brazil. It had to be Brazil, of course, with its emerging economy, enormous natural resources, great advances in technology and science and wonderful weather. The country has become a megatrend lately. Biomechanics has its megatrends too, and no place is better to catch up with them than the ISB conferences. I will mention three of them here:

Me doing my stuff at the satellite symposium on computer simulation.

Muscle synergies – neuroscience meets biomechanics

It is no secret that the central nervous system is very advanced. Christof Koch of the Allen Institute for Brain Science has nominated the brain “the most complex object in the known universe”. Of course it is debatable how and whether we can delimit objects in the universe, and one could argue (and prove in psychological tests) that several brains together work better than one. But despite the central nervous system’s amazing abilities, when you understand a little about the complexity of the control problem that must be solved in real time just to make a human walk and stay in calculated balance (or calculated imbalance, actually) it is still rather impressive how our sensory-motor system manages, and much research is devoted to this problem.

From the neuroscience perspective, scientists are measuring signals that travel up and down the neural pathways and making detailed models of them. It was nice to see a few of those presented at the conference. From the biomechanics side we try to understand how muscle forces come together to provide exactly the amount of force at exactly the right time to keep us on our feet, considering the three-dimensional dynamics we are subject to, including friction, gravity, contact forces, perturbations, acceleration, centrifugal, gyroscopic and Coriolis forces. Setting up the equations is mind boggling to most of us, let alone solving them in real time. Neuroscientists and biomechanists are addressing the same problem from two separate sides, and they may be just about to meet in the middle.
All that analysis of neural signals has brought about a new idea that is spreading like a wildfire at the moment: muscle synergies. From a special statistical processing of the signals to different muscles it is possible to realize that they are not independent. It looks like the vast majority of the signals, and we are talking several dozens, can be described by just a few free variables. If the central nervous system can make all this happen through modulation of just a small number of variables, then perhaps it is not so strange that it can manage the complexity.

Muscle synergies were the topic of several very interesting presentations and indeed also one of the focuses of my work at the moment, so stay tuned for more info in future blog entries.

Forward, inverse, static, dynamic

Other approaches that seem to come together are the different solution methods for musculoskeletal systems. The issue is that Newton’s equations allow us to find the movement if the know the forces or find the forces if the know the movement. These two different approaches have been called forward and inverse dynamics respectively, and the latter is sometimes called static optimization, which in my opinion is misleading. I’ll try not to go off on a tangent about that but merely mention than scientists tend to like their own approaches, often just because it is a hassle to try out different approaches, so a lot of arguments have been wasted on debating whether one or the other is the best way to go. For a long time, forward dynamics seemed to be chosen by a lot of scientists, but inverse dynamics started its comeback when two of the leading scientists in the field, Anderson and Pandy, published a paper entitled “Static and dynamic optimization solutions for gait are practically equivalent” [1]. Seemingly, two paths can lead to the same goal.

What happened at this conference is that the number of successful results and new approaches based on inverse dynamics seemed to be increased compared to earlier conferences, and several predictive papers were presented. The society’s president, Ton van den Bogert, presented direct collocation methods capable of predicting motion as well as forces, i.e. both sides of Newton’s equations, and my colleague, Michael Skipper Andersen showed how we can predict ground reaction forces in inverse dynamics simulations of gait and how the predicted forces actually lead to much better estimations of hip joint reaction forces. I think these developments are going to bring much more clinical applications of musculoskeletal simulation in the near future.

Clinical applications

Speaking of clinical applications, the coolest presentation from my point-of-view was by René Fluit from the University of Twente. René presented preliminary results from the TLEM/Safe project, in which an exceptionally detailed but generic lower extremity model is made specific to each patient in a surgical planning system, building on state-of-the-art technologies like AnyBody and Mimics. The picture below shows René (barely visible in the lower part of the photo) presenting the generic model (left) and the model morphed to represent a patient with severe pelvis and hip deformation (right). I am really excited about the prospects of this technology for treatment of very disabling diseases.

Another really amazing practical application of musculoskeletal simulation was by Henrik Koblauch (picture below) from the University of Copenhagen. Henrik develops amazing models of airport cargo loaders’ work situations. These are the guys who load and unload (and accosionally break) your heavy suitcase. They work in impossible postures and under very tight space constraints. Henrik was able to identify certain working postures that are especially injury-prone.

References
1. Anderson, F. C. and M. G. Pandy. Static and dynamic optimization solutions for gait are practically equivalent. J. Biomech. 34:153-161, 2001.

Digital Human Modeling

This week I attended a conference on digital human modeling (DHM) at the University of Michigan. DHM is about all sorts of models of human features and behavior, and biomechanics is an important one of these. All the papers and abstracts are freely available online: http://www.dhm2013.org/.

So what’s new on the DHM side? I’ll try to quickly recapture some of the upcoming trends.
Microsoft’s Kinect camera seems to really be a game changer. Scientists have been working on markerless mocap technology for years without managing the final breakthrough. But then Microsoft put about a million engineers on the task and probably gave them a gazillion dollars to work with, and suddenly we have a depth camera that costs about $100 and potentially replaces a $100,000 motion capture system and provides a number of other interesting features in addition. The conference contained half a dozen presentations about this technology. It is not as accurate as marker-based motion capture, but I am convinced that there are many applications, particularly in ergonomics and human behavior studies, where the accuracy of Kinect and other similar devices will be good enough.

Kinect is a depth camera meaning that it records distances between the lens and a point cloud on the surface of whatever it is observing. The additional dimension in the data means that the camera is also in essence a 3-D scanner, providing information about complex-shaped objects. The second big topic of the conference was applications and methods of processing these point clouds into geometric models of humans.

Point clouds are just a bunch of three-dimensional numbers and they are not actually very descriptive before somebody processes them and extracts useful or discernible features of what they represent. So several papers were about the processing of sets of point clouds and extraction of their descriptive features. Two important mathematical methods are repeatedly used for this: The first is Principal Component Analysis, PCA. It is a method of determining the important dimensions in a multidimensional data set. I will not go into the mathematics, but imagine you have a data set with 50 dimensions, so each feature you are describing would require 50 input numbers. With PCA you can find dimensions in this space that contain the majority of the information even if they are skew compared with the dimensions that originally describe the data. If you use these new, principal directions, you may be able to describe most of the features of the data set with just a few parameters instead of the 50 you started with. This is really interesting when we try, for instance, to make parametric models of facial features, foot shapes or the geometry of an ear.

The other important technology that comes into play with the point cloud data is Radial Basis Functions, RBF. RBF is really an interpolation technology that woks with unstructured data like scattered points, and these functions allow us to morph complex shapes of bones or skin surfaces from one person to another.
AnyBody has also embraces these new technologies. For a couple of versions we have been able to morph model based on sets of bony landmarks from CT or MRI scanners. For this we use RBF.

The final trend I want to bring out is technology convergence in the sense that human modeling systems seem to be getting ready to capitalize on each other’s features, such that users are enabled with a more complete tool. Two papers were about the similarities and differences between the systems and user needs, and your truly demonstrated how we can use advanced kinematic processing to connect otherwise incompatible models. The picture at the beginning of this blog entry, which is created by my colleague Moonki Jung, shows how we can use this idea to connect an ergonomic manikin in a CAD system with detailed musculoskeletal analysis. Now we just have to persuade the manikin manufacturers that this is a brilliant idea. The users already think so.

Huge thanks go out to the Matt Reed and Matt Parkinson and their entire staff for organizing a perfect symposium.

Borderline Biomechanics

Smashing things remains a favorite activity of boys and scientists alike. In my department, we have a very powerful air gun designed and built by my colleague, Jørgen Kepler, and it is a wonderful gadget also for investigations bordering biomechanics. We are very interested in sports equipment, because this has a profound influence the biomechanics of athletes, and if the sport in question is tennis and I even get to smash things, then I am certainly willing to play.

The research has a serious side too. Most tennis players over the introductory level will say that the racket and stringing have a profound effect on the ability to impart speed and spin to the ball, and these are key elements to winning a tennis match. So manufacturers and tennis biomechanists put much effort into understanding how the properties of rackets and strings may influence the stroke. It all takes place in a fraction of a second when the ball and string bed are impacting each other, so the phenomenon cannot be observed live, which brings us to a second piece of really cool equipment: we have a hi-speed video camera capable of up to 200,000 pictures per second. With this camera we can observe very fast phenomena.

So a group of our students, Kepler’s and mine, are setting up an investigation in which we connect experimental data with models of the impact phenomenon. The first experiment is to shoot a ball at a racket and record the impact with the video camera. Kepler came to my office the other day and, with a smirk that failed to disguise his excitement, regretted that he was unable to get the gun to shoot less than 100 m/s (= 360 km/h = 225 mph), and would I have a racket that I would volunteer for the experiment? For those of you unfamiliar with tennis, 100 m/s is well beyond the ability of even the hardest-hitting players.
I do in fact have an old racket that I no longer use, so we went and strapped it up in front of the gun. We angled it by 45 degrees compared to the movement of the ball to see how spin is created, and the spectacular result is shown in the video below.

It looks like several strings break, but it is in fact just a single break, which subsequently puts slack into the neighboring strings. We can also see the mains moving much sideways and the crosses being stretched elastically. This is very interesting because it shows that we may get more spin from the impact than popular tennis theory would indicate.

Well, Kepler did not make it far in science by being a quitter, so a new experiment was set up with the pressure cranked up by a factor of 20 to 200 Bar, resulting in a ball velocity of 260 m/s (= 936 km/h = 585 mph). This time we shot the ball perpendicularly at the racket, resulting in the damage of the picture at the head of this blog. Here’s the footage:

The power of the gun completely destroys the racket as well as the ball. In fact, both have terminal damage even before the impact. The pressure in the gun shoots a hole in the ball while inside the barrel and this is the source of the debris you see flying towards the racket before the ball appears. The pressure wave from the gun deforms the racket and destroys the frame even before the ball hits.

That was fun but I am not giving up more of my rackets for this experiment before Kepler manages to reduce the power of his gun to a more realistic level for tennis. When that happens I am expecting that we can get really good data that we can use to calibrate our simulation model and start predicting the influence of stringing parameters on the impact phenomenon.

All of this is a part of Aalborg University’s master program in sports technology. Check it out if you would like to learn constructive science by smashing things.

As simple as possible…

It was Albert Einstein who allegedly advocated that models should be as simple as possible but not simpler than that. I have been a modeler all my adult life and I very much subscribe to this point-of-view, but I do not always live by it. You see, my research group spends a lot of time developing and perfecting biomechanical models, and they tend to get more and more complex as you can see in the picture below.

This model has a little more than 1000 individually activated muscles and it does not stop there. As I am writing this, my colleagues are working on foot models, hand models, mandible models, thoracic spine models, new knee models and a new shoulder model, all of which will further increase the complexity of what you see above.

Why are we doing that if we want simplicity?

The full body model shown above is more than adequate in complexity for a lot of investigations, for instance many ergonomic studies, and in some cases much simpler models may work fine. The video below shows a 2-D cycle model that was developed in the early days of the AnyBody project. Despite its simplicity I still think it is a pretty good representation of the muscle actions in pedaling.

But if we want to zoom into details of the body, such as a foot or a knee and want to investigate the biomechanical conditions in those, then we need even more detailed models of those parts. I really cannot see model detailing coming to an end any time soon. For many applications, the models we have are still simpler than “as simple as possible”.

In geometric modeling, the dichotomy of simultaneously wanting detailed and simple models has been known for many years. For instance, a single CAD model of a car may contain millions of features (geometric details), but an application targeting the exterior surface of the car body may work better if it does not have to maintain information about the thread in the screw holes in the cylinders of the engine. Modern CAD systems deal with this problem by feature suppression; you can remove entire trees of features temporarily from the model and switch them on again when they are needed.Similarly, finite element models that analyze the mechanical behavior of the car can cope with large elements to simulate the overall vibration modes of the body but need much smaller elements to assess fatigue in the welded connection of the hinge to the door frame. Modern finite element systems can link such models together with substructuring techniques.

So what stops us from maintaining crude models for overall ergonomic investigations and much more detailed models of, for instance, a knee for ACL injury or osteoarthritis investigations? It turns out that it is very difficult to detach body parts from the rest of the body. Finite element models like the car models I mentioned before have the property that local phenomena in the model tend to influence only the local region around them; this is known in engineering as the Principle of Saint-Venant, and it means that we can remove a small hole in the model of the car body and not influence the analysis of its overall vibration modes much. Because of their high nonlinearity, musculoskeletal models do not share that property, and neither do real bodies. For instance, many therapists will tell you that a number of back and neck problems can be cured with shoe insoles. So one end of the model can easily influence the other end, and if we detach the foot from the lower leg or the lower leg from the thigh, neither of the separated parts may work the way we expected. In other words, we need to be very careful about what we are doing when we isolate body parts in models.

One possible solution is to develop popular subsets of the body such as a pelvis and two legs for gait analysis or an upper body and one arm for pitching movements, and to make sure the body parts are reasonably “tied off” in the ends where they are separated from the rest of the body. When we develop our body models, we try to enable this as much as we can by allowing users to select the presence or absence of extremities in the models. We have implemented a whole lot of additional syntactic features in our modeling language to make this happen seamlessly. For more advances cases, the best advice is what applies to modelers in all fields in general: You have to understand what you are doing.

Perhaps in the future, when computers are much faster and more powerful than today, we can simply include the entire detailed body with all its bells and whistles in all our analyses.

Nonlinearity and kinematics: shit happens

Kinematics is the part of mechanics that deals exclusively with motion. It does not consider forces at all.

First a bit of history: Kinematics is usually the first part of a biomechanical analysis and also probably one of the original roots of biomechanics. Eadweard Muybridge (1830-1904) was an English photographer who conceived the idea of studying motion through a series of still pictures. If you Google him you will discover that he, as a red-blooded Victorian gentleman, exercised his interest particularly on horses and scantily dressed ladies. Muybridge was in essence observing motions and using them to study biomechanics, and from his pioneering work sprang the motion capture technology we use today.

The nice thing about kinematics is that you can study movement with your eyes or simple equipment. In this post I want to make two points about it:

1. Kinematics is not simple but one of the trickier parts of biomechanics, and this is due to nonlinearity.
2. Kinematics is not just a stepping stone to an analysis of forces. It has a lot of value in its own right, particularly in sports.

The video below shows a very simple model, namely a four-bar mechanism. It is the mother of all mechanisms and it does not look very anatomical, but similar mechanisms are present in several different places in the skeleton and also where the body is connected to equipment, for instance a bicycle. Here’s an animation of the basic version of the mechanism.

In the mechanism above, opposite bars have equal lengths and this creates the very simple and predictable behavior of the system that you see in the video.

When we make mathematical models of nature, some phenomena are benevolent and have a predictable behavior. They are often mathematically linear or only weakly nonlinear. Such is not the case with kinematics; it is hugely nonlinear, and this fact is simultaneously a source of our endless fascination with human movement and the difficulties of simulating it.

Just think of our enjoyment of the performance of an elite athlete, i.e. somebody who can perform motions that are seemingly impossible and can only be accomplished by an extremely fine-tuned technique: A fast baseball pitch, a 300 m golf drive, a motorcycle rider racing through a curve at the very limit of tire friction, a soccer kick curving the ball around a wall of defenders and into the corner of the goal outside the reach of the goalie, Roger Federer’s forehand stroke in tennis. All of these examples are possible for trained athletes and fascinate us endlessly because they are completely beyond our reach.

So why are the best athletes able to perform so much better by small but seemingly very difficult adjustments to movements? At least a part of the answer has to do with the nonlinearity of kinematics. If you change the input a little bit, the output may become vastly different. Let us take the four-bar mechanism above and make the left bar just slightly shorter. The video below shows the surprising result.

The small change of dimension has caused a completely different kinematic behavior of the mechanism. And it gets worse. If we furthermore shorten the upper bar just a little bit, we get the following:

So tiny changes to kinematics can change the behavior of the system completely, and this is also the case for the sports performances mentioned above. The change of motion drastically influences the forces in the system, and this is what magically drives the golf ball 300 meters if you can get it completely right. The tiny adjustments make a huge difference in the result.

A general example of this is what we call kinetic chain movements that occur in all sorts of striking and kicking motions in sports. They are particularly interesting and the subject of a blog post somewhere in the near future.

It is all about nonlinearity and its unpredictable nature. It influences all of us and is the reason why events often take a completely different turn from what we expect. Shit simply happens. I leave you this time with a musical commentary, namely Lazyboy’s eloquent review of how crazy the past year was.

Perfect circle

”Sometimes circles just make sense.” This is the initial statement in a recent commercial for a knee replacement by Stryker that you can see here.

The circular motion advocated in this commercial is supposed to benefit the knee, but to people with an insight into biomechanics that message may come as a surprise. For knees, circles hardly make sense because that is not the way a real knee works.

As also illustrated in the commercial, a real knee has two condyles, which is the anatomical name for the curved bearing surfaces at the distal (far) end of the femur. These surfaces are anything but circular and the motion of the anatomical knee is quite different from a circular motion. People in biomechanics know these things, but the general public, for whom the commercial is created, does not.

So why would Stryker come up with a circular knee? Most implant manufacturers are trying to mimic the anatomical knee as closely as possible by creating implants that look geometrically very much like a real knee. It looks like Stryker is going against the flow but they may in fact be on to something. I recently attended a most interesting research symposium on knee replacements in Leuven, Belgium, and one of the issues for discussion was whether a knee replacement should be designed like an anatomical knee or like a machine part.

If we look closer into knee implants and their biomechanics, they are only superficially similar to real knees. The geometry may be almost right but the internal force bearing mechanisms are not. The implants are made of plastics, metals and ceramics, materials whose stiffness is at least an order of magnitude larger than the biological materials they replace. If the mechanics of a knee replacement is completely different from the mechanics of a real knee anyway, then why would we design a knee replacement to visually mimic a real knee? Would we ever design a joint like this for a machine? Hardly.

I think the question of whether a joint replacement made by engineers should mimic the anatomical joint it replaces as closely as possible or whether we should design the joint replacements as other technical products is one of the most intriguing at the moment, and Stryker is contributing to the field with their circular knee. We could let time and the success or misfortune of a large group of patients decide the bet. That has certainly happened in the orthopaedics field previously. My view is that this is a field where biomechanics simulation would allow us to do better than that, using models such as this one developed by the good people of the University of Southampton:

Lastly, I cannot resist the temptation of another musical comment by the amazing and amazingly beautiful Katie Melua:

“The more you scratch, the more you itch…”

It’s a living thing

People of my age will recognize the title as an Electric Light Orchestra song from their heyday (or should I say hair day?) in the 70’ies.

Since the days of ELO, we have become really good at making reliable computer models of dead things such as bridges, cars, airplanes, power plants and many other really complicated contraptions. You can find impressive examples here:  http://www.3ds.com/products/simulia/overview/
and here: http://www.ansys.com/.

I can say with complete certainty that the world would not be the same in the absence of these and similar simulation technologies. They have created an enormous revolution in the quality and functionality just about any product we use in our daily lives.

Simulating dead stuff was hard enough to do, and I should know because I did my PhD in the midst of this evolution. I can tell you that the equations are not easy. Simulating the behaviour of living things in a computer is much more complicated but also much more potent.

We simulate dead things by formulating the laws of nature in mathematical equations and solving these equations in a computer. Living things also obey the laws of nature, but there is more to them than that. Living things have a built-in control system so complicated that we usually cannot describe its behaviour mathematically, at least not completely. Humans are predictable enough for us to function together in social contexts. With some certainty I can predict that if I kick somebody in the butt, he will get mad at me, but since 30 years I have unsuccessfully tried to predict my wife’s mood swings. Some things simply defy mathematics.

Nevertheless, describing the behaviour of living things is what we endeavour to accomplish in biomechanics. Although it is not easy to do, the potential rewards are enormous, and I am not thinking about the cost of flowers for my wife but rather about the improvements of healthcare and product design that can be achieved. The point is that so many products such as all sorts of medical devices, surgical procedures, rehabilitation schemes, workplaces, furniture and many, many other things derive their value from what they do to living organisms, mostly humans. If we cannot simulate the reaction of the user to the design of the product, then we have little chance of improving it.

In biomechanics we simulate the mechanical behaviour of humans (or animals or plants). Mechanics does obey the laws of nature so in this field we can actually simulate a lot of situations such as the exercise machine shown below. With this model I can figure out which muscles are exercised depending on where the feet are placed on the machine. Cool, isn’t it?

Welcome to biomechanics

Biomechanics will have a profound influence on your life. Of course it already has influence, because motions and forces are the basis for almost anything we do. But I am thinking about the science of biomechanics. Its influence is destined to increase immensely in the future and this means opportunities now and prosperity and health in the future.

All types of orthopaedic surgery, physical therapy, medical devices, injury prevention, sports and many, many other fields are influenced by this rapidly growing technical field, and that is the motivation for this blog.

My name is John Rasmussen and I am a professor of biomechanics at Aalborg University in Denmark. I am also one of the original inventors of a biomechanics simulation system called AnyBody. I am extremely passionate about biomechanics and I am going to blog here about my personal views on biomechanics and what we can and should use it for.

I hope I can tickle your interest a little.