_Static Optimization Best Practices

Static Optimization Settings:

1. You can use IK or RRA results as input kinematics. If using IK results, you usually need to filter them, either externally or using the OpenSim analyze/static optimization field; if using RRA results, you usually do not have to filter.
2. For gait and many other motions, you need to add (append) residual actuators to the first free joint in the model (typically the ground-pelvis joint).
   
   1. There should be one actuator for each degree-of-freedom (i.e., F_X, F_Y, F_Z, M_X, M_Y, M_Z).
   2. These residual actuators are required because there is dynamic inconsistency between the estimated model accelerations and the measured ground reaction forces. This inconsistency can result from marker measurement error, differences between the geometry of the model and the subject, and inertial parameters.
   3. Running RRA will reduce—but not eliminate—these residuals. Thus, appending actuators is still necessary.
3. See How Static Optimization Works and How to Use the Static Optimization Tool for more information.

Troubleshooting:

1. If the residual actuators or the model's muscles are weak, the optimization will take a long time to converge or will never converge at all.
   
   1. If the residual actuators are weak, increase the maximum control value of a residual, while lowering its maximum force. This allows the optimizer to generate a large force (if necessary) to match accelerations, but large control values are penalized more heavily. In static optimization, ideal actuator excitations are treated as activations in the cost function.
   2. If the muscles are weak, append Coordinate Actuators to the model at the joints in the model. This will allow you to see how much "reserve" actuation is required at a given joint and then strengthen the muscles in your model accordingly.
   3. If troubleshooting a weak model and optimization is slow each time, try reducing the parameter that defines the maximum number of iterations.
2. Static optimization works internally by solving the inverse dynamics problem, then trying to solve the redundancy problem for actuators/muscles using the accelerations from the inverse dynamics solution as a constraint. If a constraint violation is reported, this could be a sign that the optimizer couldn't solve for muscle forces while enforcing the inverse dynamics solution.
   
   1. This likely means that there is noise in the data or there is a sudden jump in accelerations in one frame.
   2. In this case, you should examine the inverse dynamics solution to determine the problematic frame, and fix/interpolate the data during this portion of the motion.

Evaluating your Results:

1. Are there any large or unexpected residual actuator forces?
2. Find EMG or muscle activation data for comparison with your simulated activations. Does the timing of muscle activation/deactivation match? Are the magnitudes and patterns in good agreement?