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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(i) The first will use the same setup and input kinematics as Study 1 (including actuators), but this time;
- First Launch the Static Optimization tool and load your setup file, making sure you're still appending the file with reserve and residual actuators.
- Filter the motion by checking the Filter coordinates box. Use the default 6Hz cut-off.
- Change the output directory name to \ResultsSO_StrongActuators_Filter.
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- Launch the Static Optimization tool and load your setup file, making sure you're still appending the file with reserve and residual actuators.
- Use the motion subject_adjusted_Kinematics_q.sto (these are the kinematics generated by RRA).
- Be sure to NOT filter the kinematic data
- Change the step interval to 10
- Make sure the time is set to 0.3 to 1.5s
- Change the output folder to \ResultsSO_StrongActuators_RRA
- Save a new version of the setup file (e.g., StaticOptimization_SetupRRA)
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- Describe the changes in muscle activation for the three different motion inputs.
- Why did the residual force reduce from the Unfiltered to Filtered condition? Why did the residual force reduce further when using the model and motion from RRA?
- Which kinematic input should use for the Static Optimization analysis? Explain your reason(s).
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