Static Optimization is a method for estimating muscle activations and muscle forces that satisfy the positions, velocities, accelerations, and external forces (e.g., ground reaction forces) of a motion. The technique is called "static" since calculations are performed at each time frame, without integrating the equations of motion between time steps. Because there is no integration, Static Optimization can be very fast and efficient, but it does ignore activation dynamics and tendon compliance. (See Hicks et al., (2015) for more details regarding this and similar modeling and simulation choices and their pros and cons.)
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As with any analysis or simulation, the quality of the Static Optimization results largely depends on the inputs; : the model, motion, and forces. The model should have mass, anthropometry, and strength that represent the experimental participant; while the degrees of freedom and muscle geometry should be appropriate for the questions being asked (e.g., studying an upper body motion would require a more detailed upper extremity muscle set and skeletal geometry). The motion should contain smooth, realistic accelerations and all external forces during the motion (e.g., ground reaction forces) should be accurately measured and applied to the model. Additional forces (i.e., reserves and residuals) are often needed, but should be small enough to not confound analysis. Any issues with your inputs will give you poor results or cause the tool to fail.
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- Get an initial Static Optimization analysis running without error messages or crashes
- Improve the activation and force results by using improved motion data
- Learn how to reduce actuation from reserve and residual actuators
- Add , passive forces and observe their effect on muscle activation
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