There is no scientific consensus on when a simulation is "good enough".  Ultimately, you need to be sure you are asking the right question (such as “which muscles can contribute to knee flexion in this posture?”) and that your results are robust to variations in model parameters. In general, you should avoid questions with absolute finite answers (e.g., “how much force was generated?”) and instead use musculoskeletal simulations to understand relationships between model parameters and components. 

Nonetheless, evaluating your results is one of the most important elements of generating and analyzing a simulation of human movement. In the sections below, we provide a set of best practices for using each of the tools in the OpenSim pipeline. These best practices are intended as a starting point to help you make sure you are collecting good data and analyzing and evaluating it effectively. This list is by no means exhaustive and depends highly on the nature of the motion you are studying and the research questions you are asking. 

We've also provided a short checklist to use when evaluating your simulation.

Scaling a Model

Inverse Kinematics

Inverse Dynamics

Static Optimization

Residual Reduction

Computed Muscle Control

Forward Dynamics


Stanford University - Jennifer Hicks, Ajay Seth, Sam Hamner, Matt DeMers

University of Delaware - Jill Higginson, Brian Knarr, Amber Collins, Elisa Schrank, Chris Henderson