Introduction to Validation

Somewhere on the way to the decision of using a biomechanical model you have probably asked yourself the question: Will I be able to trust the results I get?

This is a very relevant question indeed. Computer models are just that: a model of reality, and there will always be some amount of approximation involved. The good news is that with careful modeling and the ‘right model for the right problem’ you can get very close to reality with the AnyBody Modeling System because it is tailor-made for the complexity of musculoskeletal systems.

Investigation of the accuracy of the model goes under the term ‘validation’, and this is what we will be dealing with in this tutorial. More precisely, you can expect to find the following in this tutorial:

  1. Clever ideas for validation methods.

  2. Examples of models that have been validated previously.

Several previous webcasts have presented validation studies and results, the most recent one was “Seated Human Model Validation”.

What Can Go Wrong?

Well, lots, actually. But it is helpful to try to categorize the matter into a few sources of error.

  • Errors sources in the model

  • Errors sources in the basic assumptions

  • Errors sources in the software

Errors Sources in the Model

An AnyScript model contains a lot of data, and they are all infested with some degree of inaccuracy: Geometry and mass properties of segments, assumptions about the kinematics and reactions of joints, properties and attachment points of muscles, and much more.

It has been said about biomechanics that there is a ‘right’ model for each case. For instance, there is little point in using a complex muscle model if you have little information about the muscle properties, for instance fiber lengths and pennation angles. Another consideration is the level of subject-specific accuracy. Is the purpose of the model to simulate a particular individual, or should it reflect a cross section of the population?

Model Data Uncertainties

In general, the models in the AnyScript Managed Model Repository are based on data reported in the literature. They often come from studies of one or few subjects or cadavers, and the data has little or no statistical significance. You will find the references of the data listed in the comments in the individual AnyScript files. However, the fact that a specific fiber length or muscle insertion point has been found in an individual cadaver does not mean that the value is valid for every individual or even typical.

In conclusion there is no guarantee that the values in models from any library are valid for the case you may want to analyse, and the best advice is to approach the matter with a critical mind. If results look suspicious in some part of the model, consider whether this can be due to the model input. Some typical cases are:

  • The muscle primarily responsible for carrying the load over a joint does not have sufficient strength. This can happen even in well-tested models if an unusual loading, posture or support condition that was never tested before is imposed.

  • The model has attained a posture in which the moment arm of a primary muscle erroneously becomes zero or negative. This can happen, for instance, if a wrapping muscle slides off its wrapping surface. The variation of muscle length with the joint angle reflects the moment arm, so if the moment arm is too small, then the muscle will have little length variation when the joint is articulated.

  • If the model makes use of a muscle model with stength/length variation and passive stiffness, then a tendon length that is poorly calibrated to the model can cause malfunction of its muscle. A too long tendon will cause its muscle to have little or no strength in its usual operation interval. A too short tendon will cause a muscle to excert passive force and likely cause muscles on the other side of the joint to work more than they are supposed to. Please notice that AnyBody has facilities for calibrating tendon lengths. The muscle modeling tutorial has in-depth information about these issues.

Boundary Conditions

Input to inverse dynamics is movement and boundary conditions, and these can have more influence on the result than most inexperienced modelers would expect. In fact, they are the principal source of error in many models. The human body is remarkable in its ability to make the best of the available supports, and this often creates the illusion that supports are solid while they really are not.

Consider a hand gripping a handle firmly. Apparently, the hand is rigidly connected to the handle, and you might be inclined to define a model connection between the two elements reflecting this notion. However, hands have limited strength to hold on with, and handle surfaces have limited friction to offer. If the model contrary to reality offers an effortless connection between the hand and the handle, then the model is likely to exploit this as we shall see later.

Movement Data

Recorded movement input such as motion capture data is usually in the form of positions over time. But inertia forces in the model are derived from accelerations, and to obtain accelerations, the positional data must be differentiated twice thus increasing noise and inaccuracies by two orders of magnitude. This is the topic of the first lesson of this tutorial, starting at the bottom of this page.

Errors Sources in the Basic Assumptions

As mentioned a couple of times already, the AnyBody Modeling System is based on inverse dynamics. This means that - for a given point in time - the system solves the equilibrium equations and resolves the interior muscle and joint forces. Since these time steps are solved independently of each other, the state can in principle shift abruptly from one step to the next, whereas, in reality, a change of muscle tone requires a bit of time. Force development in a muscle is the result of an electric signal from the central nervous system, which starts a chemical process in the muscle and eventually leads to contraction. All this is done within a few milliseconds, and inverse dynamics disregards this small time delay. It means that if the movement is very quick and the system predicts very rapid changes of muscle activation, then the result may not be realistic.

Another possible source of error is the distribution of force between the muscles. The body has more muscles than strictly necessary to carry most loads, so is infinitely many different combinations of muscle forces will balance the external loads. The way AnyBody picks the right one is by an optimality criterion. The system presumes that the body wants to make the best of its resources. The user has some amount of control over this criterion, but in its basic form it is a minimum fatigue criterion that distributes the loads as evenly as possible between the muscles taking their individual strengths into account. Please refer to A Study of Studies for more detailed information.

So the system basically presumes that the body has the knowledge and the desire to activate muscles optimally. This is supported by a lot of research, but the precise criterion employed by the body is a matter of continuous discussion. Furthermore, the ability to instantly choose the optimal muscle recruitment most likely requires that the movement is skilled and that the required changes of muscle activation are not faster than the electro-chemical process of muscle contraction can accommodate.

Errors in the Software

All software has bugs, and very probably this is also the case for the AnyBody Modeling System. However, in terms of muscle recruitment, the validity of the software was validated independently in 2004 in a Ph.D. thesis by Erik Forster from the University of Ulm, Germany. The thesis is available from the list of publications in the AnyScript Forum. The basic idea was to program an independent special-purpose application for gait simulation and then compare it to an identical gait model in AnyBody. If the results were identical, it would prove the correctness of the algorithms of both systems. The result was that the output data of the two systems were identical on all but a tiny fraction of the data. Closer investigation of this tiny fraction revealed that different algorithms - although mathematically similar - can produce slightly deviating results due to round-off errors.

When compared to the modeling errors and approximations due to the recruitment assumptions, errors in the software are much less likely to disturb the result of the computation significantly.

Methods of Validation

The expression ‘garbage in - garbage out’ is very much valid for biomechanical simulation. The quality of the output can never be better than the input. This means that the first step of any validation is to check the quality of the input. Input comes in the form of movements and applied forces, where the former is the more difficult. A rough check of the specified movements can be obtained by running a kinematic analysis and charting the positions, velocities and above all accelerations of characteristic points and segments in the model as illustrated for the rowing model above. Notice that proximal body parts tend to be heavier than distal body parts, so larger accelerations are plausible in the distal parts.

Gravity = 9.81 m/s^2 is a good measure to compare your values to. If the accelerations oscillate or attain unrealistic values, then the input positional information definitely needs careful reviewing and probably smoothing with a low-pass filter.

See also

Next lesson: Kinematic input validation.