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- 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.
- 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.
- 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.
- If troubleshooting a weak model and optimization is slow each time, try reducing the parameter that defines the maximum number of iterations.
- 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.
- This likely means that there is noise in the data or there is a sudden jump in accelerations in one frame.
- 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.
- If your model has passive elements (e.g., ligaments or springs), you should use OpenSim version 3.3 or later. Prior to version 3.3, the forces generated by passive elements were not properly accounted for.
Evaluating your Results:
- Are there any large or unexpected residual actuator forces?
- 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?